Sample records for qsar analysis methods

  1. SAR/QSAR methods in public health practice

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Demchuk, Eugene, E-mail: edemchuk@cdc.gov; Ruiz, Patricia; Chou, Selene

    2011-07-15

    Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protectmore » populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.« less

  2. Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

    PubMed

    Fang, Cheng; Xiao, Zhiyan

    2016-01-01

    Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

  3. A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

    PubMed

    Dong, Xialan; Ebalunode, Jerry O; Cho, Sung Jin; Zheng, Weifan

    2010-02-22

    Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

  4. QSAR and 3D QSAR of inhibitors of the epidermal growth factor receptor

    NASA Astrophysics Data System (ADS)

    Pinto-Bazurco, Mariano; Tsakovska, Ivanka; Pajeva, Ilza

    This article reports quantitative structure-activity relationships (QSAR) and 3D QSAR models of 134 structurally diverse inhibitors of the epidermal growth factor receptor (EGFR) tyrosine kinase. Free-Wilson analysis was used to derive the QSAR model. It identified the substituents in aniline, the polycyclic system, and the substituents at the 6- and 7-positions of the polycyclic system as the most important structural features. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used in the 3D QSAR modeling. The steric and electrostatic interactions proved the most important for the inhibitory effect. Both QSAR and 3D QSAR models led to consistent results. On the basis of the statistically significant models, new structures were proposed and their inhibitory activities were predicted.

  5. QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity.

    PubMed

    Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Estrada, Mario R

    2015-01-01

    Quantitative structure-activity relationships (QSAR and 3D-QSAR) have been applied in the last decade to obtain a reliable statistical model for the prediction of the anticonvulsant activities of new chemical entities. However, despite the large amount of information on QSAR, no recent review has published and discussed this data in detail. In this review, the authors provide a detailed discussion of QSAR studies that have been applied to compounds with anticonvulsant activity published between the years 2003 and 2013. They also evaluate the mathematical approaches and the main software used to develop the QSAR and 3D-QSAR model. QSAR methodologies continue to attract the attention of researchers and provide valuable information for the development of new potentially active compounds including those with anticonvulsant activity. This has been helped in part by improvements in the size and performance of computers; the development of specific software and the development of novel molecular descriptors, which have given rise to new and more predictive QSAR models. The extensive development of descriptors, and the way by which descriptor values are derived, have allowed the evolution of the QSAR methods. This evolution could strengthen the QSAR methods as an important tool in research and development of new and more potent anticonvulsant agents.

  6. Sensitivity Analysis of QSAR Models for Assessing Novel Military Compounds

    DTIC Science & Technology

    2009-01-01

    ER D C TR -0 9 -3 Strategic Environmental Research and Development Program Sensitivity Analysis of QSAR Models for Assessing Novel...Environmental Research and Development Program ERDC TR-09-3 January 2009 Sensitivity Analysis of QSAR Models for Assessing Novel Military Compound...Jay L. Clausen Cold Regions Research and Engineering Laboratory U.S. Army Engineer Research and Development Center 72 Lyme Road Hanover, NH

  7. QSAR Methods.

    PubMed

    Gini, Giuseppina

    2016-01-01

    In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

  8. Sparse QSAR modelling methods for therapeutic and regenerative medicine

    NASA Astrophysics Data System (ADS)

    Winkler, David A.

    2018-02-01

    The quantitative structure-activity relationships method was popularized by Hansch and Fujita over 50 years ago. The usefulness of the method for drug design and development has been shown in the intervening years. As it was developed initially to elucidate which molecular properties modulated the relative potency of putative agrochemicals, and at a time when computing resources were scarce, there is much scope for applying modern mathematical methods to improve the QSAR method and to extending the general concept to the discovery and optimization of bioactive molecules and materials more broadly. I describe research over the past two decades where we have rebuilt the unit operations of the QSAR method using improved mathematical techniques, and have applied this valuable platform technology to new important areas of research and industry such as nanoscience, omics technologies, advanced materials, and regenerative medicine. This paper was presented as the 2017 ACS Herman Skolnik lecture.

  9. Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.

    PubMed

    Ataide Martins, João Paulo; Rougeth de Oliveira, Marco Antônio; Oliveira de Queiroz, Mário Sérgio

    2018-06-05

    A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

  10. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

    DOE PAGES

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; ...

    2016-08-29

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide themore » recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.« less

  11. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide themore » recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.« less

  12. Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

    PubMed

    Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

    2015-01-01

    The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with

  13. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

    PubMed Central

    Zhao, Manman; Zheng, Linfeng; Qiu, Chun

    2017-01-01

    Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2 = 0.565 (cross-validated correlation coefficient) and r2 = 0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR. PMID:28630865

  14. LQTA-QSAR: a new 4D-QSAR methodology.

    PubMed

    Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

    2009-06-01

    A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

  15. The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study.

    PubMed

    Golubović, Mlađan; Lazarević, Milan; Zlatanović, Dragan; Krtinić, Dane; Stoičkov, Viktor; Mladenović, Bojan; Milić, Dragan J; Sokolović, Dušan; Veselinović, Aleksandar M

    2018-04-13

    Up to this date, there has been an ongoing debate about the mode of action of general anesthetics, which have postulated many biological sites as targets for their action. However, postoperative nausea and vomiting are common problems in which inhalational agents may have a role in their development. When a mode of action is unknown, QSAR modelling is essential in drug development. To investigate the aspects of their anesthetic, QSAR models based on the Monte Carlo method were developed for a set of polyhalogenated ethers. Until now, their anesthetic action has not been completely defined, although some hypotheses have been suggested. Therefore, a QSAR model should be developed on molecular fragments that contribute to anesthetic action. QSAR models were built on the basis of optimal molecular descriptors based on the SMILES notation and local graph invariants, whereas the Monte Carlo optimization method with three random splits into the training and test set was applied for model development. Different methods, including novel Index of ideality correlation, were applied for the determination of the robustness of the model and its predictive potential. The Monte Carlo optimization process was capable of being an efficient in silico tool for building up a robust model of good statistical quality. Molecular fragments which have both positive and negative influence on anesthetic action were determined. The presented study can be useful in the search for novel anesthetics. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

    PubMed Central

    Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

    2009-01-01

    A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

  17. Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

    PubMed Central

    Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

    2010-01-01

    Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

  18. Integration of QSAR and in vitro toxicology.

    PubMed Central

    Barratt, M D

    1998-01-01

    The principles of quantitative structure-activity relationships (QSAR) are based on the premise that the properties of a chemical are implicit in its molecular structure. Therefore, if a mechanistic hypothesis can be proposed linking a group of related chemicals with a particular toxic end point, the hypothesis can be used to define relevant parameters to establish a QSAR. Ways in which QSAR and in vitro toxicology can complement each other in development of alternatives to live animal experiments are described and illustrated by examples from acute toxicological end points. Integration of QSAR and in vitro methods is examined in the context of assessing mechanistic competence and improving the design of in vitro assays and the development of prediction models. The nature of biological variability is explored together with its implications for the selection of sets of chemicals for test development, optimization, and validation. Methods are described to support the use of data from in vivo tests that do not meet today's stringent requirements of acceptability. Integration of QSAR and in vitro methods into strategic approaches for the replacement, reduction, and refinement of the use of animals is described with examples. PMID:9599692

  19. QSAR modeling of GPCR ligands: methodologies and examples of applications.

    PubMed

    Tropsha, A; Wang, S X

    2006-01-01

    GPCR ligands represent not only one of the major classes of current drugs but the major continuing source of novel potent pharmaceutical agents. Because 3D structures of GPCRs as determined by experimental techniques are still unavailable, ligand-based drug discovery methods remain the major computational molecular modeling approaches to the analysis of growing data sets of tested GPCR ligands. This paper presents an overview of modern Quantitative Structure Activity Relationship (QSAR) modeling. We discuss the critical issue of model validation and the strategy for applying the successfully validated QSAR models to virtual screening of available chemical databases. We present several examples of applications of validated QSAR modeling approaches to GPCR ligands. We conclude with the comments on exciting developments in the QSAR modeling of GPCR ligands that focus on the study of emerging data sets of compounds with dual or even multiple activities against two or more of GPCRs.

  20. Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

    PubMed

    Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

    2014-02-01

    Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

  1. Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors

    NASA Astrophysics Data System (ADS)

    Santos-Filho, Osvaldo A.; Mishra, Rama K.; Hopfinger, A. J.

    2001-09-01

    Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target (`receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.

  2. Integrated QSAR study for inhibitors of hedgehog signal pathway against multiple cell lines:a collaborative filtering method

    PubMed Central

    2012-01-01

    Background The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. Results In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have

  3. Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method.

    PubMed

    Gao, Jun; Che, Dongsheng; Zheng, Vincent W; Zhu, Ruixin; Liu, Qi

    2012-07-31

    The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have proposed several

  4. Residual-QSAR. Implications for genotoxic carcinogenesis

    PubMed Central

    2011-01-01

    Introduction Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. Residual-QSAR Method The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis. Application and Discussions The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling

  5. CheS-Mapper 2.0 for visual validation of (Q)SAR models

    PubMed Central

    2014-01-01

    Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper.

  6. Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

    PubMed Central

    Myint, Kyaw Z.; Xie, Xiang-Qun

    2015-01-01

    This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

  7. Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

    PubMed

    Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

    2010-06-30

    QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but

  8. Towards interoperable and reproducible QSAR analyses: Exchange of datasets

    PubMed Central

    2010-01-01

    Background QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets

  9. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

    NASA Astrophysics Data System (ADS)

    Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

    2015-03-01

    Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

  10. QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

    PubMed Central

    2014-01-01

    Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. PMID:24910716

  11. AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

    PubMed

    Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

    2016-10-01

    We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

  12. QSAR models for anti-malarial activity of 4-aminoquinolines.

    PubMed

    Masand, Vijay H; Toropov, Andrey A; Toropova, Alla P; Mahajan, Devidas T

    2014-03-01

    In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

  13. Rational selection of training and test sets for the development of validated QSAR models

    NASA Astrophysics Data System (ADS)

    Golbraikh, Alexander; Shen, Min; Xiao, Zhiyan; Xiao, Yun-De; Lee, Kuo-Hsiung; Tropsha, Alexander

    2003-02-01

    Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors ( kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q 2 for the training set and accuracy of prediction ( R 2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.

  14. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

    PubMed

    Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

    2016-11-29

    In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

  15. QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines

    NASA Astrophysics Data System (ADS)

    Masand, Vijay H.; El-Sayed, Nahed N. E.; Mahajan, Devidas T.; Mercader, Andrew G.; Alafeefy, Ahmed M.; Shibi, I. G.

    2017-02-01

    In the present work, sixty substituted 2-Phenylimidazopyridines previously reported with potent anti-human African trypanosomiasis (HAT) activity were selected to build genetic algorithm (GA) based QSAR models to determine the structural features that have significant correlation with the activity. Multiple QSAR models were built using easily interpretable descriptors that are directly associated with the presence or the absence of a structural scaffold, or a specific atom. All the QSAR models have been thoroughly validated according to the OECD principles. All the QSAR models are statistically very robust (R2 = 0.80-0.87) with high external predictive ability (CCCex = 0.81-0.92). The QSAR analysis reveals that the HAT activity has good correlation with the presence of five membered rings in the molecule.

  16. Use and perceived benefits and barriers of QSAR models for REACH: findings from a questionnaire to stakeholders

    PubMed Central

    2012-01-01

    The ORCHESTRA online questionnaire on “benefits and barriers to the use of QSAR methods” addressed the academic, consultant, regulatory and industry communities potentially interested by QSAR methods in the context of REACH. Replies from more than 60 stakeholders produced some insights on the actual application of QSAR methods, and how to improve their use. Respondents state in majority that they have used QSAR methods. All have some future plans to test or use QSAR methods in accordance with their stakeholder role. The stakeholder respondents cited a total of 28 models, methods or software that they have actually applied. The three most frequently cited suites, used moreover by all the stakeholder categories, are the OECD Toolbox, EPISuite and CAESAR; all are free tools. Results suggest that stereotyped assumptions about the barriers to application of QSAR may be incorrect. Economic costs (including potential delays) are not found to be a major barrier. And only one respondent “prefers” traditional, well-known and accepted toxicological assessment methods. Information and guidance may be the keys to reinforcing use of QSAR models. Regulators appear most interested in obtaining clear explanation of the basis of the models, to provide a solid basis for decisions. Scientists appear most interested in the exploration of the scientific capabilities of the QSAR approach. Industry shows interest in obtaining reassurance that appropriate uses of QSAR will be accepted by regulators. PMID:23244245

  17. New p-methylsulfonamido phenylethylamine analogues as class III antiarrhythmic agents: design, synthesis, biological assay, and 3D-QSAR analysis.

    PubMed

    Liu, Hong; Ji, Ming; Luo, Xiaomin; Shen, Jianhua; Huang, Xiaoqin; Hua, Weiyi; Jiang, Hualiang; Chen, Kaixian

    2002-07-04

    Class III antiarrhythmic agents selectively delay the effective refractory period (ERP) and increase the transmembrane action potential duration (APD). Using dofetilide (2) as a template of class III antiarrhythmic agents, we designed and synthesized 16 methylsulfonamido phenylethylamine analogues (4a-d and 5a-l). Pharmacological assay indicated that all of these compounds showed activity for increasing the ERP in isolated animal atrium; among them, the effective concentration of compound 4a is 1.6 x 10(-8) mol/L in increasing ERP by 10 ms, slightly less potent than that of 2, 1.1 x 10(-8) mol/L. Compound 4a also produced a slightly lower change in ERP at 10(-5) M, DeltaERP% = 17.5% (DeltaERP% = 24.0% for dofetilide). On the basis of this bioassay result, these 16 compounds together with dofetilide were investigated by the three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques of comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and the hologram QSAR (HQSAR). The 3D-QSAR models were tested with another 11 compounds (4e-h and 5m-s) that we synthesized later. Results revealed that the CoMFA, CoMSIA, and HQSAR predicted activities for the 11 newly synthesized compounds that have a good correlation with their experimental value, r(2) = 0.943, 0.891, and 0.809 for the three QSAR models, respectively. This indicates that the 3D-QSAR models proved a good predictive ability and could describe the steric, electrostatic, and hydrophobic requirements for recognition forces of the receptor site. On the basis of these results, we designed and synthesized another eight new analogues of methanesulfonamido phenylethyamine (6a-h) according to the clues provided by the 3D-QSAR analyses. Pharmacological assay indicated that the effective concentrations of delaying the ERP by 10 ms of these newly designed compounds correlated well with the 3D-QSAR predicted values. It is remarkable that the percent

  18. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

    PubMed

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

    2006-08-01

    A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

  19. Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.

    PubMed

    Ruiz, Patricia; Begluitti, Gino; Tincher, Terry; Wheeler, John; Mumtaz, Moiz

    2012-07-27

    Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

  20. 3D-QSAR studies on the inhibitory activity of trimethoprim analogues against Escherichia coli dihydrofolate reductase.

    PubMed

    Vijayaraj, Ramadoss; Devi, Mekapothula Lakshmi Vasavi; Subramanian, Venkatesan; Chattaraj, Pratim Kumar

    2012-06-01

    Three-dimensional quantitative structure activity relationship (3D-QSAR) study has been carried out on the Escherichia coli DHFR inhibitors 2,4-diamino-5-(substituted-benzyl)pyrimidine derivatives to understand the structural features responsible for the improved potency. To construct highly predictive 3D-QSAR models, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were used. The predicted models show statistically significant cross-validated and non-cross-validated correlation coefficient of r2 CV and r2 nCV, respectively. The final 3D-QSAR models were validated using structurally diverse test set compounds. Analysis of the contour maps generated from CoMFA and CoMSIA methods reveals that the substitution of electronegative groups at the first and second position along with electropositive group at the third position of R2 substitution significantly increases the potency of the derivatives. The results obtained from the CoMFA and CoMSIA study delineate the substituents on the trimethoprim analogues responsible for the enhanced potency and also provide valuable directions for the design of new trimethoprim analogues with improved affinity. © 2012 John Wiley & Sons A/S.

  1. Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

    NASA Astrophysics Data System (ADS)

    Chung, K. K.; Do, D. Q.

    2010-09-01

    In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

  2. A review on principles, theory and practices of 2D-QSAR.

    PubMed

    Roy, Kunal; Das, Rudra Narayan

    2014-01-01

    The central axiom of science purports the explanation of every natural phenomenon using all possible logics coming from pure as well as mixed scientific background. The quantitative structure-activity relationship (QSAR) analysis is a study correlating the behavioral manifestation of compounds with their structures employing the interdisciplinary knowledge of chemistry, mathematics, biology as well as physics. Several studies have attempted to mathematically correlate the chemistry and property (physicochemical/ biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters termed as descriptors. The dimensionality of the descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. The most interesting feature of predictive QSAR models is that the behavior of any new or even hypothesized molecule can be predicted by the use of the mathematical equations. The phrase "2D-QSAR" signifies development of QSAR models using 2D-descriptors. Such predictor variables are the most widely practised ones because of their simple and direct mathematical algorithmic nature involving no time consuming energy computations and having reproducible operability. 2D-descriptors have a deluge of contributions in extracting chemical attributes and they are also capable of representing the 3D molecular features to some extent; although in no case they should be considered as the ultimate one, since they often suffer from the problems of intercorrelation, insufficient chemical information as well as lack of interpretation. However, by following rational approaches, novel 2D-descriptors may be developed to obviate various existing problems giving potential 2D-QSAR equations, thereby solving the innumerable chemical mysteries still unexplored.

  3. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    PubMed

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  4. Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes

    NASA Astrophysics Data System (ADS)

    Norinder, Ulf

    1990-12-01

    An experimental design based 3-D QSAR analysis using a combination of principal component and PLS analysis is presented and applied to human corticosteroid-binding globulin complexes. The predictive capability of the created model is good. The technique can also be used as guidance when selecting new compounds to be investigated.

  5. TOXICO-CHEMINFORMATICS AND QSAR MODELING OF ...

    EPA Pesticide Factsheets

    This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models. This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models.

  6. In silico study of in vitro GPCR assays by QSAR modeling ...

    EPA Pesticide Factsheets

    The U.S. EPA is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays (the ToxCast program). One goal is to prioritize chemicals for more detailed analyses based on activity in molecular initiating events (MIE) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than this set of chemicals. Thus, there is a need to fill data gaps with in silico methods, and quantitative structure-activity relationships (QSARs) are a proven and cost effective approach to predict biological activity. ToxCast in turn provides relatively large datasets that are ideal for training and testing QSAR models. The overall goal of the study described here was to develop QSAR models to fill the data gaps in a larger environmental database of ~32k structures. The specific aim of the current work was to build QSAR models for 18 G-Protein Coupled Receptor (GPCR) assays, part of the aminergic category. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least squares d

  7. Receptor-based 3D QSAR analysis of estrogen receptor ligands - merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

    NASA Astrophysics Data System (ADS)

    Sippl, Wolfgang

    2000-08-01

    One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient ( r 2 = 0.617, q 2 LOO = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained ( r 2 = 0.991, q 2 LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment ( r 2 = 0.951, q 2 LOO = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.

  8. QSAR studies in the discovery of novel type-II diabetic therapies.

    PubMed

    Abuhammad, Areej; Taha, Mutasem O

    2016-01-01

    Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity. This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds. Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

  9. Design of Novel Chemotherapeutic Agents Targeting Checkpoint Kinase 1 Using 3D-QSAR Modeling and Molecular Docking Methods.

    PubMed

    Balupuri, Anand; Balasubramanian, Pavithra K; Cho, Seung J

    2016-01-01

    Checkpoint kinase 1 (Chk1) has emerged as a potential therapeutic target for design and development of novel anticancer drugs. Herein, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses on a series of diazacarbazoles to design potent Chk1 inhibitors. 3D-QSAR models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Docking studies were performed using AutoDock. The best CoMFA and CoMSIA models exhibited cross-validated correlation coefficient (q2) values of 0.631 and 0.585, and non-cross-validated correlation coefficient (r2) values of 0.933 and 0.900, respectively. CoMFA and CoMSIA models showed reasonable external predictabilities (r2 pred) of 0.672 and 0.513, respectively. A satisfactory performance in the various internal and external validation techniques indicated the reliability and robustness of the best model. Docking studies were performed to explore the binding mode of inhibitors inside the active site of Chk1. Molecular docking revealed that hydrogen bond interactions with Lys38, Glu85 and Cys87 are essential for Chk1 inhibitory activity. The binding interaction patterns observed during docking studies were complementary to 3D-QSAR results. Information obtained from the contour map analysis was utilized to design novel potent Chk1 inhibitors. Their activities and binding affinities were predicted using the derived model and docking studies. Designed inhibitors were proposed as potential candidates for experimental synthesis.

  10. An ensemble model of QSAR tools for regulatory risk assessment.

    PubMed

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0

  11. Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking

    NASA Astrophysics Data System (ADS)

    Li, Peizhen; Tian, Yueli; Zhai, Honglin; Deng, Fangfang; Xie, Meihong; Zhang, Xiaoyun

    2013-11-01

    Non-purine derivatives have been shown to be promising novel drug candidates as xanthine oxidase inhibitors. Based on three-dimensional quantitative structure-activity relationship (3D-QSAR) methods including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), two 3D-QSAR models for a series of non-purine xanthine oxidase (XO) inhibitors were established, and their reliability was supported by statistical parameters. Combined 3D-QSAR modeling and the results of molecular docking between non-purine xanthine oxidase inhibitors and XO, the main factors that influenced activity of inhibitors were investigated, and the obtained results could explain known experimental facts. Furthermore, several new potential inhibitors with higher activity predicted were designed, which based on our analyses, and were supported by the simulation of molecular docking. This study provided some useful information for the development of non-purine xanthine oxidase inhibitors with novel structures.

  12. Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening.

    PubMed

    Al-Masri, Ihab M; Mohammad, Mohammad K; Taha, Mutasem O

    2008-11-01

    Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

  13. Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking.

    PubMed

    Chen, H F; Dong, X C; Zen, B S; Gao, K; Yuan, S G; Panaye, A; Doucet, J P; Fan, B T

    2003-08-01

    An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

  14. Ligand-based 3D QSAR analysis of reactivation potency of mono- and bis-pyridinium aldoximes toward VX-inhibited rat acetylcholinesterase.

    PubMed

    Dolezal, Rafael; Korabecny, Jan; Malinak, David; Honegr, Jan; Musilek, Kamil; Kuca, Kamil

    2015-03-01

    To predict unknown reactivation potencies of 12 mono- and bis-pyridinium aldoximes for VX-inhibited rat acetylcholinesterase (rAChE), three-dimensional quantitative structure-activity relationship (3D QSAR) analysis has been carried out. Utilizing molecular interaction fields (MIFs) calculated by molecular mechanical (MMFF94) and quantum chemical (B3LYP/6-31G*) methods, two satisfactory ligand-based CoMFA models have been developed: 1. R(2)=0.9989, Q(LOO)(2)=0.9090, Q(LTO)(2)=0.8921, Q(LMO(20%))(2)=0.8853, R(ext)(2)=0.9259, SDEP(ext)=6.8938; 2. R(2)=0.9962, Q(LOO)(2)=0.9368, Q(LTO)(2)=0.9298, Q(LMO(20%))(2)=0.9248, R(ext)(2)=0.8905, SDEP(ext)=6.6756. High statistical significance of the 3D QSAR models has been achieved through the application of several data noise reduction techniques (i.e. smart region definition SRD, fractional factor design FFD, uninformative/iterative variable elimination UVE/IVE) on the original MIFs. Besides the ligand-based CoMFA models, an alignment molecular set constructed by flexible molecular docking has been also studied. The contour maps as well as the predicted reactivation potencies resulting from 3D QSAR analyses help better understand which structural features are associated with increased reactivation potency of studied compounds. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.

    PubMed

    Du, Qi-Shi; Huang, Ri-Bo; Wei, Yu-Tuo; Pang, Zong-Wen; Du, Li-Qin; Chou, Kuo-Chen

    2009-01-30

    In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus. (c) 2008 Wiley Periodicals, Inc.

  16. QSAR models based on quantum topological molecular similarity.

    PubMed

    Popelier, P L A; Smith, P J

    2006-07-01

    A new method called quantum topological molecular similarity (QTMS) was fairly recently proposed [J. Chem. Inf. Comp. Sc., 41, 2001, 764] to construct a variety of medicinal, ecological and physical organic QSAR/QSPRs. QTMS method uses quantum chemical topology (QCT) to define electronic descriptors drawn from modern ab initio wave functions of geometry-optimised molecules. It was shown that the current abundance of computing power can be utilised to inject realistic descriptors into QSAR/QSPRs. In this article we study seven datasets of medicinal interest : the dissociation constants (pK(a)) for a set of substituted imidazolines , the pK(a) of imidazoles , the ability of a set of indole derivatives to displace [(3)H] flunitrazepam from binding to bovine cortical membranes , the influenza inhibition constants for a set of benzimidazoles , the interaction constants for a set of amides and the enzyme liver alcohol dehydrogenase , the natriuretic activity of sulphonamide carbonic anhydrase inhibitors and the toxicity of a series of benzyl alcohols. A partial least square analysis in conjunction with a genetic algorithm delivered excellent models. They are also able to highlight the active site, of the ligand or the molecule whose structure determines the activity. The advantages and limitations of QTMS are discussed.

  17. The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

    PubMed

    Tseng, Yufeng J; Hopfinger, Anton J; Esposito, Emilio Xavier

    2012-01-01

    The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

  18. The discovery of indicator variables for QSAR using inductive logic programming

    NASA Astrophysics Data System (ADS)

    King, Ross D.; Srinivasan, Ashwin

    1997-11-01

    A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P < 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.

  19. An ensemble model of QSAR tools for regulatory risk assessment

    DOE PAGES

    Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; ...

    2016-09-22

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflictingmore » predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0

  20. Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors.

    PubMed

    Kumar, Ashwani; Chauhan, Shilpi

    2017-01-01

    SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R 2  = 0.8350, Q 2 test  = 0.7491 for the test set and R 2  = 0.9655, Q 2 ext  = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. DemQSAR: predicting human volume of distribution and clearance of drugs

    NASA Astrophysics Data System (ADS)

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is

  2. DemQSAR: predicting human volume of distribution and clearance of drugs.

    PubMed

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is

  3. Novel fragment-based QSAR modeling and combinatorial design of pyrazole-derived CRK3 inhibitors as potent antileishmanials.

    PubMed

    Goyal, Sukriti; Dhanjal, Jaspreet K; Tyagi, Chetna; Goyal, Manisha; Grover, Abhinav

    2014-07-01

    The CRK3 cyclin-dependent kinase of Leishmania plays an important role in regulating the cell-cycle progression at the G2/M phase checkpoint transition, proliferation, and viability inside the host macrophage. In this study, a novel fragment-based QSAR model has been developed using 22 pyrazole-derived compounds exhibiting inhibitory activity against Leishmanial CRK3. Unlike other QSAR methods, this fragment-based method gives flexibility to study the relationship between molecular fragments of interest and their contribution for the variation in the biological response by evaluating cross-term fragment descriptors. Based on the fragment-based QSAR model, a combinatorial library was generated, and top two compounds were reported after predicting their activity. The QSAR model showed satisfactory statistical parameters for the data set (r(2) = 0.8752, q(2) = 0.6690, F-ratio = 30.37, and pred_r(2) = 0.8632) with four descriptors describing the nature of substituent groups and the environment of the substitution site. Evaluation of the model implied that electron-rich substitution at R1 position improves the inhibitory activity, while decline in inhibitory activity was observed in presence of nitrogen at R2 position. The analysis carried out in this study provides a substantial basis for consideration of the designed pyrazole-based leads as potent antileishmanial drugs. © 2014 John Wiley & Sons A/S.

  4. Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms.

    PubMed

    Chen, Hongming; Carlsson, Lars; Eriksson, Mats; Varkonyi, Peter; Norinder, Ulf; Nilsson, Ingemar

    2013-06-24

    A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project.

  5. Critical body residues linked to octanol-water partitioning, organism composition, and LC50 QSARs: meta-analysis and model.

    PubMed

    Hendriks, A Jan; Traas, Theo P; Huijbregts, Mark A J

    2005-05-01

    To protect thousands of species from thousands of chemicals released in the environment, various risk assessment tools have been developed. Here, we link quantitative structure-activity relationships (QSARs) for response concentrations in water (LC50) to critical concentrations in organisms (C50) by a model for accumulation in lipid or non-lipid phases versus water Kpw. The model indicates that affinity for neutral body components such as storage fat yields steep Kpw-Kow relationships, whereas slopes for accumulation in polar phases such as proteins are gentle. This pattern is confirmed by LC50 QSARs for different modes of action, such as neutral versus polar narcotics and organochlorine versus organophosphor insecticides. LC50 QSARs were all between 0.00002 and 0.2Kow(-1). After calibrating the model with the intercepts and, for the first time also, with the slopes of the LC50 QSARs, critical concentrations in organisms C50 are calculated and compared to an independent validation data set. About 60% of the variability in lethal body burdens C50 is explained by the model. Explanations for differences between estimated and measured levels for 11 modes of action are discussed. In particular, relationships between the critical concentrations in organisms C50 and chemical (Kow) or species (lipid content) characteristics are specified and tested. The analysis combines different models proposed before and provides a substantial extension of the data set in comparison to previous work. Moreover, the concept is applied to species (e.g., plants, lean animals) and substances (e.g., specific modes of action) that were scarcely studied quantitatively so far.

  6. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

    PubMed Central

    Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

    2015-01-01

    Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

  7. Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.

    PubMed

    Potemkin, Andrey V; Grishina, Maria A; Potemkin, Vladimir A

    2017-01-01

    In 1979, R.D.Cramer and M.Milne made a first realization of 3D comparison of molecules by aligning them in space and by mapping their molecular fields to a 3D grid. Further, this approach was developed as the DYLOMMS (Dynamic Lattice- Oriented Molecular Modelling System) approach. In 1984, H.Wold and S.Wold proposed the use of partial least squares (PLS) analysis, instead of principal component analysis, to correlate the field values with biological activities. Then, in 1988, the method which was called CoMFA (Comparative Molecular Field Analysis) was introduced and the appropriate software became commercially available. Since 1988, a lot of 3D QSAR methods, algorithms and their modifications are introduced for solving of virtual drug discovery problems (e.g., CoMSIA, CoMMA, HINT, HASL, GOLPE, GRID, PARM, Raptor, BiS, CiS, ConGO,). All the methods can be divided into two groups (classes):1. Methods studying the exterior of molecules; 2) Methods studying the interior of molecules. A series of grid-based computational technologies for Continual Molecular Interior analysis (CoMIn) are invented in the current paper. The grid-based analysis is fulfilled by means of a lattice construction analogously to many other grid-based methods. The further continual elucidation of molecular structure is performed in various ways. (i) In terms of intermolecular interactions potentials. This can be represented as a superposition of Coulomb, Van der Waals interactions and hydrogen bonds. All the potentials are well known continual functions and their values can be determined in all lattice points for a molecule. (ii) In the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum

  8. QSAR modeling: where have you been? Where are you going to?

    PubMed

    Cherkasov, Artem; Muratov, Eugene N; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-06-26

    Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

  9. QSAR Modeling: Where have you been? Where are you going to?

    PubMed Central

    Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I.; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C.; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E.; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  10. Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

    PubMed

    Martínez-Santiago, O; Marrero-Ponce, Y; Vivas-Reyes, R; Rivera-Borroto, O M; Hurtado, E; Treto-Suarez, M A; Ramos, Y; Vergara-Murillo, F; Orozco-Ugarriza, M E; Martínez-López, Y

    2017-05-01

    Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively

  11. Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1.

    PubMed

    Fatima, Sabiha; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2012-08-01

    Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r², q²(loo) and r²(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r²(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.

  12. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

    PubMed Central

    Song, Fucheng; Zhang, Anling; Liang, Hui; Cui, Lianhua; Li, Wenlian; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

    2016-01-01

    A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. PMID:27854309

  13. Determination of receptor-bound drug conformations by QSAR using flexible fitting to derive a molecular similarity index

    NASA Astrophysics Data System (ADS)

    Montanari, C. A.; Tute, M. S.; Beezer, A. E.; Mitchell, J. C.

    1996-02-01

    Results are presented for a QSAR analysis of bisamidines, using a similarity index as descriptor. The method allows for differences in conformation of bisamidines at the receptor site to be taken into consideration. In particular, it has been suggested by others that pentamidine binds in the minor groove of DNA in a so-called isohelical conformation, and our QSAR supports this suggestion. The molecular similarity index for comparison of molecules can be used as a parameter for correlating and hence rationalising the activity as well as suggesting the design of bioactive molecules. The studied compounds had been evaluated for potency against Leishmania mexicana amazonensis, and this potency was used as a dependent variable in a series of QSAR analyses. For the calculation of similarity indexes, each analogue was in turn superimposed on a chosen lead compound in a reference conformation, either extended or isohelical, maximising overlap and hence similarity by flexible fitting.

  14. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

    PubMed

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

  15. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    PubMed Central

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  16. Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.

    PubMed

    Nendza, Monika; Müller, Martin; Wenzel, Andrea

    2017-03-22

    Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR

  17. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

    PubMed Central

    2011-01-01

    Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of

  18. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

    PubMed

    Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

    2011-07-28

    Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models

  19. 3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors

    NASA Astrophysics Data System (ADS)

    Assefa, Haregewein; Kamath, Shantaram; Buolamwini, John K.

    2003-08-01

    The overexpression and/or mutation of the epidermal growth factor receptor (EGFR) tyrosine kinase has been observed in many human solid tumors, and is under intense investigation as a novel anticancer molecular target. Comparative 3D-QSAR analyses using different alignments were undertaken employing comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) for 122 anilinoquinazoline and 50 anilinoquinoline inhibitors of EGFR kinase. The SYBYL multifit alignment rule was applied to three different conformational templates, two obtained from a MacroModel Monte Carlo conformational search, and one from the bound conformation of erlotinib in complex with EGFR in the X-ray crystal structure. In addition, a flexible ligand docking alignment obtained with the GOLD docking program, and a novel flexible receptor-guided consensus dynamics alignment obtained with the DISCOVER program in the INSIGHTII modeling package were also investigated. 3D-QSAR models with q2 values up to 0.70 and r2 values up to 0.97 were obtained. Among the 4-anilinoquinazoline set, the q2 values were similar, but the ability of the different conformational models to predict the activities of an external test set varied considerably. In this regard, the model derived using the X-ray crystallographically determined bioactive conformation of erlotinib afforded the best predictive model. Electrostatic, hydrophobic and H-bond donor descriptors contributed the most to the QSAR models of the 4-anilinoquinazolines, whereas electrostatic, hydrophobic and H-bond acceptor descriptors contributed the most to the 4-anilinoquinoline QSAR, particularly the H-bond acceptor descriptor. A novel receptor-guided consensus dynamics alignment has also been introduced for 3D-QSAR studies. This new alignment method may incorporate to some extent ligand-receptor induced fit effects into 3D-QSAR models.

  20. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

    PubMed

    Li, Jiazhong; Gramatica, Paola

    2010-11-01

    Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common

  1. The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

    PubMed

    Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

    2016-10-01

    The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of

  2. QSAR studies of benzofuran/benzothiophene biphenyl derivatives as inhibitors of PTPase-1B

    PubMed Central

    Kaushik, D.; Kumar, R.; Saxena, A. K.

    2010-01-01

    Objectives: Insulin resistance is associated with a defect in protein tyrosine phosphorylation in the insulin signal transduction cascade. The PTPase enzyme dephosphorylates the active form of the insulin receptor and thus attenuates its tyrosine kinase activity, therefore, the need for a potent PTPase inhibitor exists, with the intention of which the QSAR was performed. Materials and Methods: Quantitative structure-activity relationship (QSAR) has been established on a series of 106 compounds considering 27 variables, for novel biphenyl analogs, using the SYSTAT (Version 7.0) software, for their protein tyrosine phosphatase (PTPase-1B) inhibitor activity, in order to understand the essential structural requirement for binding with the receptor. Results: Among several regression models, one per series was selected on the basis of a high correlation coefficient (r, 0.86), least standard deviation (s, 0.234), and a high value of significance for the maximum number of subjects (n, 101). Conclusions: The influence of the different physicochemical parameters of the substituents in various positions has been discussed by generating the best QSAR model using multiple regression analysis, and the information thus obtained from the present study can be used to design and predict more potent molecules as PTPase-1B inhibitors, prior to their synthesis. PMID:21814427

  3. A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

    PubMed

    Algamal, Z Y; Lee, M H

    2017-01-01

    A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

  4. Quantitative structure-activity relationships (QSARs) for the transformation of organic micropollutants during oxidative water treatment.

    PubMed

    Lee, Yunho; von Gunten, Urs

    2012-12-01

    Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  5. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

    PubMed

    Winkler, David A; Le, Tu C

    2017-01-01

    Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

    PubMed

    Tuppurainen, Kari; Viisas, Marja; Laatikainen, Reino; Peräkylä, Mikael

    2002-01-01

    A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.

  7. The use of QSAR methods for determination of n-octanol/water partition coefficient using the example of hydroxyester HE-1

    NASA Astrophysics Data System (ADS)

    Guziałowska-Tic, Joanna

    2017-10-01

    According to the Directive of the European Parliament and of the Council concerning the protection of animals used for scientific purposes, the number of experiments involving the use of animals needs to be reduced. The methods which can replace animal testing include computational prediction methods, for instance, the quantitative structure-activity relationships (QSAR). These methods are designed to find a cohesive relationship between differences in the values of the properties of molecules and the biological activity of a series of test compounds. This paper compares the results of the author's own results of examination on the n-octanol/water coefficient for the hydroxyester HE-1 with those generated by means of three models: Kowwin, MlogP, AlogP. The test results indicate that, in the case of molecular similarity, the highest determination coefficient was obtained for the model MlogP and the lowest root-mean square error was obtained for the Kowwin method. When comparing the mean logP value obtained using the QSAR models with the value resulting from the author's own experiments, it was observed that the best conformity was that recorded for the model AlogP, where relative error was 15.2%.

  8. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test.

    PubMed

    Klüver, Nils; Vogs, Carolina; Altenburger, Rolf; Escher, Beate I; Scholz, Stefan

    2016-12-01

    Fish embryos have become a popular model in ecotoxicology and toxicology. The fish embryo acute toxicity test (FET) with the zebrafish embryo was recently adopted by the OECD as technical guideline TG 236 and a large database of concentrations causing 50% lethality (LC 50 ) is available in the literature. Quantitative Structure-Activity Relationships (QSARs) of baseline toxicity (also called narcosis) are helpful to estimate the minimum toxicity of chemicals to be tested and to identify excess toxicity in existing data sets. Here, we analyzed an existing fish embryo toxicity database and established a QSAR for fish embryo LC 50 using chemicals that were independently classified to act according to the non-specific mode of action of baseline toxicity. The octanol-water partition coefficient K ow is commonly applied to discriminate between non-polar and polar narcotics. Replacing the K ow by the liposome-water partition coefficient K lipw yielded a common QSAR for polar and non-polar baseline toxicants. This developed baseline toxicity QSAR was applied to compare the final mode of action (MOA) assignment of 132 chemicals. Further, we included the analysis of internal lethal concentration (ILC 50 ) and chemical activity (La 50 ) as complementary approaches to evaluate the robustness of the FET baseline toxicity. The analysis of the FET dataset revealed that specifically acting and reactive chemicals converged towards the baseline toxicity QSAR with increasing hydrophobicity. The developed FET baseline toxicity QSAR can be used to identify specifically acting or reactive compounds by determination of the toxic ratio and in combination with appropriate endpoints to infer the MOA for chemicals. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. 3D-QSAR and molecular docking studies on HIV protease inhibitors

    NASA Astrophysics Data System (ADS)

    Tong, Jianbo; Wu, Yingji; Bai, Min; Zhan, Pei

    2017-02-01

    In order to well understand the chemical-biological interactions governing their activities toward HIV protease activity, QSAR models of 34 cyclic-urea derivatives with inhibitory HIV were developed. The quantitative structure activity relationship (QSAR) model was built by using comparative molecular similarity indices analysis (CoMSIA) technique. And the best CoMSIA model has rcv2, rncv2 values of 0.586 and 0.931 for cross-validated and non-cross-validated. The predictive ability of CoMSIA model was further validated by a test set of 7 compounds, giving rpred2 value of 0.973. Docking studies were used to find the actual conformations of chemicals in active site of HIV protease, as well as the binding mode pattern to the binding site in protease enzyme. The information provided by 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 34 cyclic-urea derivatives and help to design potential anti-HIV protease molecules.

  10. Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Politi, Regina; Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599; Rusyn, Ivan, E-mail: iir@unc.edu

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict bindingmore » affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  12. Beware of external validation! - A Comparative Study of Several Validation Techniques used in QSAR Modelling.

    PubMed

    Majumdar, Subhabrata; Basak, Subhash C

    2018-04-26

    Proper validation is an important aspect of QSAR modelling. External validation is one of the widely used validation methods in QSAR where the model is built on a subset of the data and validated on the rest of the samples. However, its effectiveness for datasets with a small number of samples but large number of predictors remains suspect. Calculating hundreds or thousands of molecular descriptors using currently available software has become the norm in QSAR research, owing to computational advances in the past few decades. Thus, for n chemical compounds and p descriptors calculated for each molecule, the typical chemometric dataset today has high value of p but small n (i.e. n < p). Motivated by the evidence of inadequacies of external validation in estimating the true predictive capability of a statistical model in recent literature, this paper performs an extensive and comparative study of this method with several other validation techniques. We compared four validation methods: leave-one-out, K-fold, external and multi-split validation, using statistical models built using the LASSO regression, which simultaneously performs variable selection and modelling. We used 300 simulated datasets and one real dataset of 95 congeneric amine mutagens for this evaluation. External validation metrics have high variation among different random splits of the data, hence are not recommended for predictive QSAR models. LOO has the overall best performance among all validation methods applied in our scenario. Results from external validation are too unstable for the datasets we analyzed. Based on our findings, we recommend using the LOO procedure for validating QSAR predictive models built on high-dimensional small-sample data. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  13. QSAR, molecular docking studies of thiophene and imidazopyridine derivatives as polo-like kinase 1 inhibitors

    NASA Astrophysics Data System (ADS)

    Cao, Shandong

    2012-08-01

    The purpose of the present study was to develop in silico models allowing for a reliable prediction of polo-like kinase inhibitors based on a large diverse dataset of 136 compounds. As an effective method, quantitative structure activity relationship (QSAR) was applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The proposed QSAR models showed reasonable predictivity of thiophene analogs (Rcv2=0.533, Rpred2=0.845) and included four molecular descriptors, namely IC3, RDF075m, Mor02m and R4e+. The optimal model for imidazopyridine derivatives (Rcv2=0.776, Rpred2=0.876) was shown to perform good in prediction accuracy, using GATS2m and BEHe1 descriptors. Analysis of the contour maps helped to identify structural requirements for the inhibitors and served as a basis for the design of the next generation of the inhibitor analogues. Docking studies were also employed to position the inhibitors into the polo-like kinase active site to determine the most probable binding mode. These studies may help to understand the factors influencing the binding affinity of chemicals and to develop alternative methods for prescreening and designing of polo-like kinase inhibitors.

  14. Does rational selection of training and test sets improve the outcome of QSAR modeling?

    PubMed

    Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

    2012-10-22

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

  15. Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis

    PubMed Central

    Putz, Mihai V.; Ionaşcu, Cosmin; Putz, Ana-Maria; Ostafe, Vasile

    2011-01-01

    Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD50], i.e., ASA=f(X1SA,X2SA,…)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARASA=f(A−ASA,X1SA,X2SA,…). We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. PMID:21954348

  16. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...

  17. The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

    NASA Astrophysics Data System (ADS)

    Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

    2014-10-01

    Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

  18. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

    PubMed

    Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors.

  19. Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies

    NASA Astrophysics Data System (ADS)

    Manoharan, Prabu; Vijayan, R. S. K.; Ghoshal, Nanda

    2010-10-01

    The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables ( X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.

  20. Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies.

    PubMed

    Manoharan, Prabu; Vijayan, R S K; Ghoshal, Nanda

    2010-10-01

    The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables (X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.

  1. Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Li, Xiaolin; Ye, Li; Wang, Xiaoxiang

    2012-12-15

    Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q{sup 2}) was 0.571 andmore » non-cross-validation correlation coefficient (r{sup 2}) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results. -- Highlights: ► The thyroid hormone activities of HO-PBDEs were studied by 3D-QSAR. ► The binding modes between HO-PBDEs and TRβ were explored. ► 3D-QSAR, molecular docking, and molecular dynamics (MD) methods were performed.« less

  2. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

    PubMed

    Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A; Nicklaus, Marc C; Zakharov, Alexey V; Poroikov, Vladimir V

    2015-07-27

    Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

  3. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

    PubMed Central

    Cronin, Mark T D; Walker, John D; Jaworska, Joanna S; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing. PMID:12896861

  4. Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Petko, Kirill; Metelytsia, Larisa

    2016-09-01

    Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. © 2016 John Wiley & Sons A/S.

  5. (Q)SARs to predict environmental toxicities: current status and future needs.

    PubMed

    Cronin, Mark T D

    2017-03-22

    The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

  6. A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

    PubMed

    Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

    2018-02-01

    Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

  7. A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

    NASA Astrophysics Data System (ADS)

    Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

    2018-02-01

    Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

  8. Application of 3D-QSAR in the rational design of receptor ligands and enzyme inhibitors.

    PubMed

    Mor, Marco; Rivara, Silvia; Lodola, Alessio; Lorenzi, Simone; Bordi, Fabrizio; Plazzi, Pier Vincenzo; Spadoni, Gilberto; Bedini, Annalida; Duranti, Andrea; Tontini, Andrea; Tarzia, Giorgio

    2005-11-01

    Quantitative structure-activity relationships (QSARs) are frequently employed in medicinal chemistry projects, both to rationalize structure-activity relationships (SAR) for known series of compounds and to help in the design of innovative structures endowed with desired pharmacological actions. As a difference from the so-called structure-based drug design tools, they do not require the knowledge of the biological target structure, but are based on the comparison of drug structural features, thus being defined ligand-based drug design tools. In the 3D-QSAR approach, structural descriptors are calculated from molecular models of the ligands, as interaction fields within a three-dimensional (3D) lattice of points surrounding the ligand structure. These descriptors are collected in a large X matrix, which is submitted to multivariate analysis to look for correlations with biological activity. Like for other QSARs, the reliability and usefulness of the correlation models depends on the validity of the assumptions and on the quality of the data. A careful selection of compounds and pharmacological data can improve the application of 3D-QSAR analysis in drug design. Some examples of the application of CoMFA and CoMSIA approaches to the SAR study and design of receptor or enzyme ligands is described, pointing the attention to the fields of melatonin receptor ligands and FAAH inhibitors.

  9. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

    2007-07-01

    Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200

  10. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    PubMed Central

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  11. The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Muratov, Eugene N.; Artemenko, Anatoly G.; Gorb, Leonid; Qasim, Mohammad; Leszczynski, Jerzy

    2008-10-01

    The present study applies the Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD50) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory ( R 2 = 0.96-0.98; Q 2 = 0.91-0.93; R 2 test = 0.89-0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C-H fragments of the aromatic ring.

  12. Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors.

    PubMed

    Abuhamdah, Sawsan; Habash, Maha; Taha, Mutasem O

    2013-12-01

    Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

  13. From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

    PubMed Central

    Zhu, Hao

    2017-01-01

    Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

  14. Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

    PubMed

    Tropsha, Alexander; Golbraikh, Alexander

    2007-01-01

    Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied as an evaluative approach, i.e., with the focus on developing retrospective and explanatory models of existing data. Model extrapolation was considered if only in hypothetical sense in terms of potential modifications of known biologically active chemicals that could improve compounds' activity. This critical review re-examines the strategy and the output of the modern QSAR modeling approaches. We provide examples and arguments suggesting that current methodologies may afford robust and validated models capable of accurate prediction of compound properties for molecules not included in the training sets. We discuss a data-analytical modeling workflow developed in our laboratory that incorporates modules for combinatorial QSAR model development (i.e., using all possible binary combinations of available descriptor sets and statistical data modeling techniques), rigorous model validation, and virtual screening of available chemical databases to identify novel biologically active compounds. Our approach places particular emphasis on model validation as well as the need to define model applicability domains in the chemistry space. We present examples of studies where the application of rigorously validated QSAR models to virtual screening identified computational hits that were confirmed by subsequent experimental investigations. The emerging focus of QSAR modeling on target property forecasting brings it forward as predictive, as opposed to evaluative, modeling approach.

  15. Application of 3D-QSAR for identification of descriptors defining bioactivity of antimicrobial peptides.

    PubMed

    Bhonsle, Jayendra B; Venugopal, Divakaramenon; Huddler, Donald P; Magill, Alan J; Hicks, Rickey P

    2007-12-27

    In our laboratory, a series of antimicrobial peptides have been developed, where the resulting 3D-physicochemical properties are controlled by the placement of amino acids with well-defined properties (hydrophobicity, charge density, electrostatic potential, and so on) at specific locations along the peptide backbone. These peptides exhibited different in vitro activity against Staphylococcus aureus (SA) and Mycobacterium ranae (MR) bacteria. We hypothesized that the differences in the biological activity is a direct manifestation of different physicochemical interactions that occur between the peptides and the cell membranes of the bacteria. 3D-QSAR analysis has shown that, within this series, specific physicochemical properties are responsible for antibacterial activity and selectivity. There are five physicochemical properties specific to the SA QSAR model, while five properties are specific to the MR QSAR model. These results support the hypothesis that, for any particular AMP, organism selectivity and potency are controlled by the chemical composition of the target cell membrane.

  16. Synthesis, biological evaluation, QSAR study and molecular docking of novel N-(4-amino carbonylpiperazinyl) (thio)phosphoramide derivatives as cholinesterase inhibitors.

    PubMed

    Gholivand, Khodayar; Ebrahimi Valmoozi, Ali Asghar; Bonsaii, Mahyar

    2014-06-01

    Novel (thio)phosphoramidate derivatives based on piperidincarboxamide with the general formula of (NH2-C(O)-C5H9N)-P(X=O,S)R1R2 (1-5) and (NH2-C(O)-C5H9N)2-P(O)R (6-9) were synthesized and characterized by (31)P, (13)C, (1)H NMR, IR spectroscopy. Furthermore, the crystal structure of compound (NH2-C(O)-C5H9N)2-P(O)(OC6H5) (6) was investigated. The activities of derivatives on cholinesterases (ChE) were determined using a modified Ellman's method. Also the mixed-type mechanisms of these compounds were evaluated by Lineweaver-Burk plots. Molecular docking and quantitative structure-activity relationship (QSAR) were used to understand the relationship between molecular structural features and anti-ChE activity, and to predict the binding affinity of phosphoramido-piperidinecarboxamides (PAPCAs) to ChE receptors. From molecular docking analysis, noncovalent interactions especially hydrogen bonding as well as hydrophobic was found between PAPCAs and ChE. Based on the docking results, appropriate molecular structural parameters were adopted to develop a QSAR model. DFT-QSAR models for ChE enzymes demonstrated the importance of electrophilicity parameter in describing the anti-AChE and anti-BChE activities of the synthesized compounds. The correlation matrix of QSAR models and docking analysis confirmed that electrophilicity descriptor can control the influence of the hydrophobic properties of P=(O, S) and CO functional groups of PAPCA derivatives in the inhibition of human ChE enzymes. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

    PubMed Central

    Cronin, Mark T D; Jaworska, Joanna S; Walker, John D; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches. PMID:12896862

  18. Experimental and QSAR study on the surface activities of alkyl imidazoline surfactants

    NASA Astrophysics Data System (ADS)

    Kong, Xiangjun; Qian, Chengduo; Fan, Weiyu; Liang, Zupei

    2018-03-01

    15 alkyl imidazoline surfactants with different structures were synthesized and their critical micelle concentration (CMC) and surface tension under the CMC (σcmc) in aqueous solution were measured at 298 K. 54 kinds of molecular structure descriptors were selected as independent variables and the quantitative structure-activity relationship (QSAR) between surface activities of alkyl imidazoline and molecular structure were built through the genetic function approximation (GFA) method. Experimental results showed that the maximum surface excess of alkyl imidazoline molecules at the gas-liquid interface increased and the area occupied by each surfactant molecule and the free energies of micellization ΔGm decreased with increasing carbon number (NC) of the hydrophobic chain or decreasing hydrophilicity of counterions, which resulted in a CMC and σcmc decrease, while the log CMC and NC had a linear relationship and a negative correlation. The GFA-QSAR model, which was generated by a training set composed of 13 kinds of alkyl imidazoline though GFA method regression analysis, was highly correlated with predicted values and experimental values of the CMC. The correlation coefficient R was 0.9991, which means high prediction accuracy. The prediction error of 2 kinds of alkyl imidazoline CMCs in the Validation Set that quantitatively analyzed the influence of the alkyl imidazoline molecular structure on the CMC was less than 4%.

  19. Predicting physical properties of emerging compounds with limited physical and chemical data: QSAR model uncertainty and applicability to military munitions.

    PubMed

    Bennett, Erin R; Clausen, Jay; Linkov, Eugene; Linkov, Igor

    2009-11-01

    Reliable, up-front information on physical and biological properties of emerging materials is essential before making a decision and investment to formulate, synthesize, scale-up, test, and manufacture a new material for use in both military and civilian applications. Multiple quantitative structure-activity relationships (QSARs) software tools are available for predicting a material's physical/chemical properties and environmental effects. Even though information on emerging materials is often limited, QSAR software output is treated without sufficient uncertainty analysis. We hypothesize that uncertainty and variability in material properties and uncertainty in model prediction can be too large to provide meaningful results. To test this hypothesis, we predicted octanol water partitioning coefficients (logP) for multiple, similar compounds with limited physical-chemical properties using six different commercial logP calculators (KOWWIN, MarvinSketch, ACD/Labs, ALogP, CLogP, SPARC). Analysis was done for materials with largely uncertain properties that were similar, based on molecular formula, to military compounds (RDX, BTTN, TNT) and pharmaceuticals (Carbamazepine, Gemfibrizol). We have also compared QSAR modeling results for a well-studied pesticide and pesticide breakdown product (Atrazine, DDE). Our analysis shows variability due to structural variations of the emerging chemicals may be several orders of magnitude. The model uncertainty across six software packages was very high (10 orders of magnitude) for emerging materials while it was low for traditional chemicals (e.g. Atrazine). Thus the use of QSAR models for emerging materials screening requires extensive model validation and coupling QSAR output with available empirical data and other relevant information.

  20. Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version.

    PubMed

    Furuhama, A; Toida, T; Nishikawa, N; Aoki, Y; Yoshioka, Y; Shiraishi, H

    2010-07-01

    The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure-activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r(2 )> 0.7, RMSE 5, give acceptable q(2) values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.

  1. Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.

    PubMed

    Zhou, Peng; Wang, Congcong; Tian, Feifei; Ren, Yanrong; Yang, Chao; Huang, Jian

    2013-01-01

    Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.

  2. QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

    PubMed

    Basant, Nikita; Gupta, Shikha

    2017-06-01

    The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

  3. Evolution of the international workshops on quantitative structure-activity relationships (QSARs) in environmental toxicology.

    PubMed

    Kaiser, K L E

    2007-01-01

    This presentation will review the evolution of the workshops from a scientific and personal perspective. From their modest beginning in 1983, the workshops have developed into larger international meetings, regularly held every two years. Their initial focus on the aquatic sphere soon expanded to include properties and effects on atmospheric and terrestrial species, including man. Concurrent with this broadening of their scientific scope, the workshops have become an important forum for the early dissemination of all aspects of qualitative and quantitative structure-activity research in ecotoxicology and human health effects. Over the last few decades, the field of quantitative structure/activity relationships (QSARs) has quickly emerged as a major scientific method in understanding the properties and effects of chemicals on the environment and human health. From substances that only affect cell membranes to those that bind strongly to a specific enzyme, QSARs provides insight into the biological effects and chemical and physical properties of substances. QSARs are useful for delineating the quantitative changes in biological effects resulting from minor but systematic variations of the structure of a compound with a specific mode of action. In addition, more holistic approaches are being devised that result in our ability to predict the effects of structurally unrelated compounds with (potentially) different modes of action. Research in QSAR environmental toxicology has led to many improvements in the manufacturing, use, and disposal of chemicals. Furthermore, it has led to national policies and international agreements, from use restrictions or outright bans of compounds, such as polychlorinated biphenyls (PCBs), mirex, and highly chlorinated pesticides (e.g. DDT, dieldrin) for the protection of avian predators, to alternatives for ozone-depleting compounds, to better waste treatment systems, to more powerful and specific acting drugs. Most of the recent advances

  4. Quantitative structure activity relationship (QSAR) of piperine analogs for bacterial NorA efflux pump inhibitors.

    PubMed

    Nargotra, Amit; Sharma, Sujata; Koul, Jawahir Lal; Sangwan, Pyare Lal; Khan, Inshad Ali; Kumar, Ashwani; Taneja, Subhash Chander; Koul, Surrinder

    2009-10-01

    Quantitative structure activity relationship (QSAR) analysis of piperine analogs as inhibitors of efflux pump NorA from Staphylococcus aureus has been performed in order to obtain a highly accurate model enabling prediction of inhibition of S. aureus NorA of new chemical entities from natural sources as well as synthetic ones. Algorithm based on genetic function approximation method of variable selection in Cerius2 was used to generate the model. Among several types of descriptors viz., topological, spatial, thermodynamic, information content and E-state indices that were considered in generating the QSAR model, three descriptors such as partial negative surface area of the compounds, area of the molecular shadow in the XZ plane and heat of formation of the molecules resulted in a statistically significant model with r(2)=0.962 and cross-validation parameter q(2)=0.917. The validation of the QSAR models was done by cross-validation, leave-25%-out and external test set prediction. The theoretical approach indicates that the increase in the exposed partial negative surface area increases the inhibitory activity of the compound against NorA whereas the area of the molecular shadow in the XZ plane is inversely proportional to the inhibitory activity. This model also explains the relationship of the heat of formation of the compound with the inhibitory activity. The model is not only able to predict the activity of new compounds but also explains the important regions in the molecules in quantitative manner.

  5. 2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

    2017-09-01

    The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

  6. 3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments.

    PubMed

    Pandey, Gyanendra; Saxena, Anil K

    2006-01-01

    A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.

  7. Molecular modeling-driven approach for identification of Janus kinase 1 inhibitors through 3D-QSAR, docking and molecular dynamics simulations.

    PubMed

    Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

    2017-10-01

    Janus kinase 1 (JAK 1) belongs to the JAK family of intracellular nonreceptor tyrosine kinase. JAK-signal transducer and activator of transcription (JAK-STAT) pathway mediate signaling by cytokines, which control survival, proliferation and differentiation of a variety of cells. Three-dimensional quantitative structure activity relationship (3 D-QSAR), molecular docking and molecular dynamics (MD) methods was carried out on a dataset of Janus kinase 1(JAK 1) inhibitors. Ligands were constructed and docked into the active site of protein using GLIDE 5.6. Best docked poses were selected after analysis for further 3 D-QSAR analysis using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodology. Employing 60 molecules in the training set, 3 D-QSAR models were generate that showed good statistical reliability, which is clearly observed in terms of r 2 ncv and q 2 loo values. The predictive ability of these models was determined using a test set of 25 molecules that gave acceptable predictive correlation (r 2 Pred ) values. The key amino acid residues were identified by means of molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good consonance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the reasonable modification of molecules in order to design more efficient JAK 1 inhibitors. The developed models are expected to provide some directives for further synthesis of highly effective JAK 1 inhibitors.

  8. Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

    PubMed

    Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

    2016-04-01

    The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. 3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM.

    PubMed

    Ghafouri, Hamidreza; Ranjbar, Mohsen; Sakhteman, Amirhossein

    2017-08-01

    A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q 2 LOO-CV =1, R 2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Genetic training of network using chaos concept: application to QSAR studies of vibration modes of tetrahedral halides.

    PubMed

    Lu, Qingzhang; Shen, Guoli; Yu, Ruqin

    2002-11-15

    The chaotic dynamical system is introduced in genetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (nu(1)(A1) and nu(2)(E)) of halides [MX(4)](epsilon). The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples. Copyright 2002 Wiley Periodicals, Inc.

  11. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research

    PubMed Central

    Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

    2010-01-01

    Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all

  12. New public QSAR model for carcinogenicity

    PubMed Central

    2010-01-01

    Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions

  13. Polyphenol fatty acid esters as serine protease inhibitors: a quantum-chemical QSAR analysis.

    PubMed

    Viskupicova, Jana; Danihelova, Martina; Majekova, Magdalena; Liptaj, Tibor; Sturdik, Ernest

    2012-12-01

    We investigated the ability of polyphenol fatty acid esters to inhibit the activity of serine proteases trypsin, thrombin, elastase and urokinase. Potent protease inhibition in micromolar range was displayed by rutin and rutin derivatives esterified with medium and long chain, mono- and polyunsaturated fatty acids (1e-m), followed by phloridzin and esculin esters with medium and long fatty acid chain length (2a-d, 3a-d), while unmodified compounds showed only little or no effect. QSAR study of the compounds tested provided the most significant parameters for individual inhibition activities, i.e. number of hydrogen bond donors for urokinase, molecular volume for thrombin, and solvation energy for elastase. According to the statistical analysis, the action of elastase inhibitors is opposed to those of urokinase and thrombin. Cluster analysis showed two groups of compounds: original polyphenols together with rutin esters with short fatty acid chain length and rutin esters with long fatty acid chain length.

  14. 4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

    PubMed

    Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

    2012-02-01

    A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

  15. OPERA: A free and open source QSAR tool for predicting physicochemical properties and environmental fate endpoints

    EPA Science Inventory

    Collecting the chemical structures and data for necessary QSAR modeling is facilitated by available public databases and open data. However, QSAR model performance is dependent on the quality of data and modeling methodology used. This study developed robust QSAR models for physi...

  16. A search for sources of drug resistance by the 4D-QSAR analysis of a set of antimalarial dihydrofolate reductase inhibitors

    NASA Astrophysics Data System (ADS)

    Santos-Filho, Osvaldo Andrade; Hopfinger, Anton J.

    2001-01-01

    A set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines were studied using four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. The corresponding biological activities of these compounds include IC50 inhibition constants for both the wild type, and a specific mutant type of Plasmodium falciparum dihydrofolate reductase (DHFR). Two thousand conformations of each analog were sampled to generate a conformational ensemble profile (CEP) from a molecular dynamics simulation (MDS) of 100,000 conformer trajectory states. Each sampled conformation was placed in a 1 Å cubic grid cell lattice for each of five trial alignments. The frequency of occupation of each grid cell was computed for each of six types of pharmacophore groups of atoms of each compound. These grid cell occupancy descriptors (GCODs) were then used as a descriptor pool to construct 4D-QSAR models. Models for inhibition of both the `wild' type and the mutant enzyme were generated which provide detailed spatial pharmacophore requirements for inhibition in terms of atom types and their corresponding relative locations in space. The 4D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the Plasmodium falciparum DHFR to current antimalarials. One feature identified is a slightly different binding alignment of the ligands to the mutant form of the enzyme as compared to the wild type.

  17. Environmental Containment Property Estimation Using QSARs in an Expert System

    DTIC Science & Technology

    1991-10-15

    economical method to estimate aqueous solubility, octanol/ water partition coefficients, vapor pressures, organic carbon, normalized soil sorption...PROPERTY ESTIMATION USING QSARs IN AN EXPERT SYSTEM William J. Doucette Mark S. Holt Doug J. Denne Joan E. McLean Utah State University Utah Water ...persistence of a chemical are aqueous solubility, octanol/ water partition coefficient, soil/ water sorption coefficient, Henry’s Law constant

  18. Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

    PubMed

    Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M Natália D S; Cagide Fajin, J Luis; Morell, Carlos; Ruiz, Reinaldo Molina; Cañizares-Carmenate, Yudith; Dominguez, Elena Rosa

    2008-01-01

    Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

  19. GTM-Based QSAR Models and Their Applicability Domains.

    PubMed

    Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

    2015-06-01

    In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

    PubMed

    Toropov, Andrey A; Toropova, Alla P; Puzyn, Tomasz; Benfenati, Emilio; Gini, Giuseppina; Leszczynska, Danuta; Leszczynski, Jerzy

    2013-06-01

    Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Proteins QSAR with Markov average electrostatic potentials.

    PubMed

    González-Díaz, Humberto; Uriarte, Eugenio

    2005-11-15

    Classic physicochemical and topological indices have been largely used in small molecules QSAR but less in proteins QSAR. In this study, a Markov model is used to calculate, for the first time, average electrostatic potentials xik for an indirect interaction between aminoacids placed at topologic distances k within a given protein backbone. The short-term average stochastic potential xi1 for 53 Arc repressor mutants was used to model the effect of Alanine scanning on thermal stability. The Arc repressor is a model protein of relevance for biochemical studies on bioorganics and medicinal chemistry. A linear discriminant analysis model developed correctly classified 43 out of 53, 81.1% of proteins according to their thermal stability. More specifically, the model classified 20/28, 71.4% of proteins with near wild-type stability and 23/25, 92.0% of proteins with reduced stability. Moreover, predictability in cross-validation procedures was of 81.0%. Expansion of the electrostatic potential in the series xi0, xi1, xi2, and xi3, justified the use of the abrupt truncation approach, being the overall accuracy >70.0% for xi0 but equal for xi1, xi2, and xi3. The xi1 model compared favorably with respect to others based on D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, with less than 77.0% of accuracy [Ramos de Armas, R.; González-Díaz, H.; Molina, R.; Uriarte, E. Protein Struct. Func. Bioinf.2004, 56, 715]. The xi1 model also has more tractable interpretation than others based on Markovian negentropies and stochastic moments. Finally, the model is notably simpler than the two models based on quadratic and linear indices. Both models, reported by Marrero-Ponce et al., use four-to-five time more descriptors. Introduction of average stochastic potentials may be useful for QSAR applications; having xik amenable physical interpretation and being very effective.

  2. Lacosamide derivatives with anticonvulsant activity as carbonic anhydrase inhibitors. Molecular modeling, docking and QSAR analysis.

    PubMed

    Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Duchowicz, Pablo R; Torrens, Francisco; Estrada, Mario R

    2014-01-01

    Lacosamide is an anticonvulsant drug which presents carbonic anhydrase inhibition. In this paper, we analyzed the apparent relationship between both activities performing a molecular modeling, docking and QSAR studies on 18 lacosamide derivatives with known anticonvulsant activity. Docking results suggested the zinc-binding site of carbonic anhydrase is a possible target of lacosamide and lacosamide derivatives making favorable Van der Waals interactions with Asn67, Gln92, Phe131 and Thr200. The mathematical models revealed a poor relationship between the anticonvulsant activity and molecular descriptors obtained from DFT and docking calculations. However, a QSAR model was developed using Dragon software descriptors. The statistic parameters of the model are: correlation coefficient, R=0.957 and standard deviation, S=0.162. Our results provide new valuable information regarding the relationship between both activities and contribute important insights into the essential molecular requirements for the anticonvulsant activity.

  3. Combined 3D-QSAR modeling and molecular docking studies on pyrrole-indolin-2-ones as Aurora A kinase inhibitors.

    PubMed

    Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

    2011-01-01

    Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.

  4. Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

    PubMed

    Roy, Kunal; Mitra, Indrani

    2011-07-01

    Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

  6. Quantum chemical parameters in QSAR: what do I use when?

    USGS Publications Warehouse

    Hickey, James P.; Ostrander, Gary K.

    1996-01-01

    This chapter provides a brief overview of the numerous quantum chemical parameters that have been/are currently being used in quantitative structure activity relationships (QSAR), along with a representative bibliography. The parameters will be grouped according to their mechanistic interpretations, and representative biological and physical chemical applications will be mentioned. Parmater computation methods and the appropriate software are highlighted, as are sources for software.

  7. Reliable Prescreening of Candidate NerveAgent Prophylaxes via 3D QSAR

    DTIC Science & Technology

    2005-12-31

    recognize and predict prospective toxicity among covalent -binding AChE inhibitors of potential application to nerve agent prophylaxis and...is below since many authors do not follow the 200 word limit 14. SUBJECT TERMS nerve agents , acetylcholinesterase, prophylaxis, QSAR, virtual...Report: Reliable Prescreening of Candidate NerveAgent Prophylaxes via 3D QSAR Report Title ABSTRACT Organophosphorus (OP) nerve agents are among the

  8. QSAR of phytochemicals for the design of better drugs.

    PubMed

    Kar, Supratik; Roy, Kunal

    2012-10-01

    Phytochemicals have been the single most prolific source of leads for the development of new drug entities from the dawn of the drug discovery. They cover a wide range of therapeutic indications with a great diversity of chemical structures. The research fraternity still believes in exploring the phytochemicals for new drug discovery. Application of molecular biological techniques has increased the availability of novel compounds that can be conveniently isolated from natural sources. Combinatorial chemistry approaches are being applied based on phytochemical scaffolds to create screening libraries that closely resemble drug-like compounds. In silico techniques like quantitative structure-activity relationships (QSAR), pharmacophore and virtual screening are playing crucial and rate accelerating steps for the better drug design in modern era. QSAR models of different classes of phytochemicals covering different therapeutic areas are thoroughly discussed in the review. Further, the authors have enlisted all the available phytochemical databases for the convenience of researchers working in the area. This review justifies the need to develop more QSAR models for the design of better drugs from phytochemicals. Technical drawbacks associated with phytochemical research have been lessened, and there are better opportunities to explore the biological activity of previously inaccessible sources of phytochemicals although there is still the need to reduce the time and cost involvement in such exercise. The future possibilities for the integration of ethnopharmacology with QSAR, place us at an exciting stage that will allow us to explore plant sources worldwide and design better drugs.

  9. Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

    PubMed

    Rácz, A; Bajusz, D; Héberger, K

    2015-01-01

    Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

  10. Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein-protein inhibitors of p53-MDM2.

    PubMed

    Goyal, Sukriti; Grover, Sonam; Dhanjal, Jaspreet Kaur; Tyagi, Chetna; Goyal, Manisha; Grover, Abhinav

    2014-06-01

    Tumour suppressor p53 is known to play a central role in prevention of tumour development, DNA repair, senescence and apoptosis which is in normal cells maintained by negative feedback regulator MDM2 (Murine Double Minute 2). In case of dysfunctioning of this regulatory loop, tumour development starts thus resulting in cancerous condition. Inhibition of p53-MDM2 binding would result in activation of the tumour suppressor. In this study, a novel robust fragment-based QSAR model has been developed for piperidinone derived compounds experimentally known to inhibit p53-MDM2 interaction. The QSAR model developed showed satisfactory statistical parameters for the experimentally reported dataset (r(2)=0.9415, q(2)=0.8958, pred_r(2)=0.8894 and F-test=112.7314), thus judging the robustness of the model. Low standard error values (r(2)_se=0.3003, q(2)_se=0.4009 and pred_r(2)_se=0.3315) confirmed the accuracy of the developed model. The regression equation obtained constituted three descriptors (R2-DeltaEpsilonA, R1-RotatableBondCount and R2-SssOCount), two of which had positive contribution while third showed negative correlation. Based on the developed QSAR model, a combinatorial library was generated and activities of the compounds were predicted. These compounds were docked with MDM2 and two top scoring compounds with binding affinities of -10.13 and -9.80kcal/mol were selected. The binding modes of actions of these complexes were analyzed using molecular dynamics simulations. Analysis of the developed fragment-based QSAR model revealed that addition of unsaturated electronegative groups at R2 site and groups with more rotatable bonds at R1 improved the inhibitory activity of these potent lead compounds. The detailed analysis carried out in this study provides a considerable basis for the design and development of novel piperidinone-based lead molecules against cancer and also provides mechanistic insights into their mode of actions. Copyright © 2014 Elsevier Inc. All

  11. The importance of data curation on QSAR Modeling - PHYSPROP open data as a case study. (QSAR 2016)

    EPA Science Inventory

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These ...

  12. A MODE-OF-ACTION-BASED QSAR APPROACH TO IMPROVE UNDERSTANDING OF DEVELOPMENTAL TOXICITY

    EPA Science Inventory

    QSAR models of developmental toxicity (devtox) have met with limited regulatory acceptance due to the use of ill-defined endpoints, lack of biological interpretability, and poor model performance. More generally, the lack of biological inference of many QSAR models is often due t...

  13. The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity.

    PubMed

    Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko

    2008-06-01

    One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

  14. 3D QSAR models built on structure-based alignments of Abl tyrosine kinase inhibitors.

    PubMed

    Falchi, Federico; Manetti, Fabrizio; Carraro, Fabio; Naldini, Antonella; Maga, Giovanni; Crespan, Emmanuele; Schenone, Silvia; Bruno, Olga; Brullo, Chiara; Botta, Maurizio

    2009-06-01

    Quality QSAR: A combination of docking calculations and a statistical approach toward Abl inhibitors resulted in a 3D QSAR model, the analysis of which led to the identification of ligand portions important for affinity. New compounds designed on the basis of the model were found to have very good affinity for the target, providing further validation of the model itself.The X-ray crystallographic coordinates of the Abl tyrosine kinase domain in its active, inactive, and Src-like inactive conformations were used as targets to simulate the binding mode of a large series of pyrazolo[3,4-d]pyrimidines (known Abl inhibitors) by means of GOLD software. Receptor-based alignments provided by molecular docking calculations were submitted to a GRID-GOLPE protocol to generate 3D QSAR models. Analysis of the results showed that the models based on the inactive and Src-like inactive conformations had very poor statistical parameters, whereas the sole model based on the active conformation of Abl was characterized by significant internal and external predictive ability. Subsequent analysis of GOLPE PLS pseudo-coefficient contour plots of this model gave us a better understanding of the relationships between structure and affinity, providing suggestions for the next optimization process. On the basis of these results, new compounds were designed according to the hydrophobic and hydrogen bond donor and acceptor contours, and were found to have improved enzymatic and cellular activity with respect to parent compounds. Additional biological assays confirmed the important role of the selected compounds as inhibitors of cell proliferation in leukemia cells.

  15. Statistical molecular design of balanced compound libraries for QSAR modeling.

    PubMed

    Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

    2010-01-01

    A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

  16. Rational design of methicillin resistance staphylococcus aureus inhibitors through 3D-QSAR, molecular docking and molecular dynamics simulations.

    PubMed

    Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

    2018-04-01

    Staphylococcus aureus is a gram positive bacterium. It is the leading cause of skin and respiratory infections, osteomyelitis, Ritter's disease, endocarditis, and bacteraemia in the developed world. We employed combined studies of 3D QSAR, molecular docking which are validated by molecular dynamics simulations and in silico ADME prediction have been performed on Isothiazoloquinolones inhibitors against methicillin resistance Staphylococcus aureus. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study was applied using comparative molecular field analysis (CoMFA) with Q 2 of 0.578, R 2 of 0.988, and comparative molecular similarity indices analysis (CoMSIA) with Q 2 of 0.554, R 2 of 0.975. The predictive ability of these model was determined using a test set of molecules that gave acceptable predictive correlation (r 2 Pred) values 0.55 and 0.57 of CoMFA and CoMSIA respectively. Docking, simulations were employed to position the inhibitors into protein active site to find out the most probable binding mode and most reliable conformations. Developed models and Docking methods provide guidance to design molecules with enhanced activity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Structure–activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

    PubMed Central

    Lakhlili, Wiame; Yasri, Abdelaziz; Ibrahimi, Azeddine

    2016-01-01

    The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be a challenging task. The quantitative structure–activity relationship (QSAR) approach is a very useful and widespread technique for ligand-based drug design, which can be used to identify novel and potent mTOR inhibitors. In this study, we performed two-dimensional QSAR tests, and molecular docking validation tests of a series of mTOR ATP-competitive inhibitors to elucidate their structural properties associated with their activity. The QSAR tests were performed using partial least square method with a correlation coefficient of r2=0.799 and a cross-validation of q2=0.714. The chemical library screening was done by associating ligand-based to structure-based approach using the three-dimensional structure of mTOR developed by homology modeling. We were able to select 22 compounds from two databases as inhibitors of the mTOR kinase active site. We believe that the method and applications highlighted in this study will help future efforts toward the design of selective ATP-competitive inhibitors. PMID:27980424

  18. How to Deal with Low-Resolution Target Structures: Using SAR, Ensemble Docking, Hydropathic Analysis, and 3D-QSAR to Definitively Map the αβ-Tubulin Colchicine Site

    PubMed Central

    Da, Chenxiao; Mooberry, Susan L.; Gupton, John T.; Kellogg, Glen E.

    2013-01-01

    αβ-tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q2 of 0.616, r2 of 0.949 and r2pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 Å available for αβ-tubulin. PMID:23961916

  19. Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

    NASA Astrophysics Data System (ADS)

    Gramatica, Paola

    This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

  20. Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

    PubMed

    Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu

    2015-04-01

    Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

  1. OPERA: A QSAR tool for physicochemical properties and environmental fate predictions (ACS Spring meeting)

    EPA Science Inventory

    The collection of chemical structures and associated experimental data for QSAR modeling is facilitated by the increasing number and size of public databases. However, the performance of QSAR models highly depends on the quality of the data used and the modeling methodology. The ...

  2. A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists

    NASA Astrophysics Data System (ADS)

    Belvisi, Laura; Bravi, Gianpaolo; Catalano, Giovanna; Mabilia, Massimo; Salimbeni, Aldo; Scolastico, Carlo

    1996-12-01

    A series of non-peptide angiotensin II receptor antagonists was investigated with the aim of developing a 3D QSAR model using comparative molecular field analysis descriptors and approaches. The main goals of the study were dictated by an interest in methodologies and an understanding of the binding requirements to the AT1 receptor. Consistency with the previously derived activity models was always checked to contemporarily test the validity of the various hypotheses. The specific conformations chosen for the study, the procedures invoked to superimpose all structures, the conditions employed to generate steric and electrostatic field values and the various PCA/PLS runs are discussed in detail. The effect of experimental design techniques to select objects (molecules) and variables (descriptors) with respect to the predictive power of the QSAR models derived was especially analysed.

  3. Synthesis, characterization and anti-microbial evaluation of Cu(II), Ni(II), Pt(II) and Pd(II) sulfonylhydrazone complexes; 2D-QSAR analysis of Ni(II) complexes of sulfonylhydrazone derivatives

    NASA Astrophysics Data System (ADS)

    Özbek, Neslihan; Alyar, Saliha; Alyar, Hamit; Şahin, Ertan; Karacan, Nurcan

    2013-05-01

    Copper(II), nickel(II), platinum(II) and palladium(II) complexes with 2-hydroxy-1-naphthaldehyde-N-methylpropanesulfonylhydrazone (nafpsmh) derived from propanesulfonic acid-1-methylhydrazide (psmh) were synthesized, their structure were identified, and antimicrobial activity of the compounds was screened against three Gram-positive and three Gram-negative bacteria. The results of antimicrobial studies indicate that Pt(II) and Pd(II) complexes showed the most activity against all bacteria. The crystal structure of 2-hydroxy-1-naphthaldehyde-N-methylpropanesulfonylhydrazone (nafpsmh) was also investigated by X-ray analysis. A series of Ni(II) sulfonyl hydrazone complexes (1-33) was synthesized and tested in vitro against Escherichia coli and Staphylococcus aureus. Their antimicrobial activities were used in the QSAR analysis. Four-parameter QSAR models revealed that nucleophilic reaction index for Ni and O atoms, and HOMO-LUMO energy gap play key roles in the antimicrobial activity.

  4. N-tuple topological/geometric cutoffs for 3D N-linear algebraic molecular codifications: variability, linear independence and QSAR analysis.

    PubMed

    García-Jacas, C R; Marrero-Ponce, Y; Barigye, S J; Hernández-Ortega, T; Cabrera-Leyva, L; Fernández-Castillo, A

    2016-12-01

    Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria. First, an analysis of a diverse chemical space for the most common values of topological/Euclidean-geometric distances, bond/dihedral angles, triangle/quadrilateral perimeters, triangle area and volume was performed in order to determine the intervals to take into account in the cutoff procedures. A variability analysis based on Shannon's entropy reveals that better distribution patterns are attained with the descriptors based on the cutoffs proposed (QuBiLS-MIDAS NQ-MDs) with regard to the results obtained when all inter-atomic relations are considered (QuBiLS-MIDAS KA-MDs - 'Keep All'). A principal component analysis shows that the novel molecular cutoffs codify chemical information captured by the respective QuBiLS-MIDAS KA-MDs, as well as information not captured by the latter. Lastly, a QSAR study to obtain deeper knowledge of the contribution of the proposed methods was carried out, using four molecular datasets (steroids (STER), angiotensin converting enzyme (ACE), thermolysin inhibitors (THER) and thrombin inhibitors (THR)) widely used as benchmarks in the evaluation of several methodologies. One to four variable QSAR models based on multiple linear regression were developed for each compound dataset following the original division into training and test sets. The

  5. Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

    PubMed Central

    Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

    2011-01-01

    Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r2cv values of 0.726 and 0.566, and r2 values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed. PMID:21673910

  6. The interplay between QSAR/QSPR studies and partial order ranking and formal concept analyses.

    PubMed

    Carlsen, Lars

    2009-04-17

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental - and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example.

  7. The Interplay between QSAR/QSPR Studies and Partial Order Ranking and Formal Concept Analyses

    PubMed Central

    Carlsen, Lars

    2009-01-01

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example. PMID:19468330

  8. Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

    PubMed

    Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

    2016-06-01

    Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.

    PubMed

    Soufan, Othman; Ba-Alawi, Wail; Magana-Mora, Arturo; Essack, Magbubah; Bajic, Vladimir B

    2018-06-14

    High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F 1 score of 76.68% and 76.53%, respectively. Furthermore, DPubChem builds interaction networks that highlight novel predicted links between chemical compounds and biological assays. Using such a network, DPubChem successfully suggested a novel drug for the Niemann-Pick type C disease. DPubChem is freely available at www.cbrc.kaust.edu.sa/dpubchem .

  10. CURRENT PRACTICES IN QSAR DEVELOPMENT AND APPLICATIONS

    EPA Science Inventory

    Current Practices in QSAR Development and Applications

    Although it is commonly assumed that the structure and properties of a single chemical determines its activity in a particular biological system, it is only through study of how biological activity varies with changes...

  11. Combined molecular modelling and 3D-QSAR study for understanding the inhibition of NQO1 by heterocyclic quinone derivatives.

    PubMed

    López-Lira, Claudia; Alzate-Morales, Jans H; Paulino, Margot; Mella-Raipán, Jaime; Salas, Cristian O; Tapia, Ricardo A; Soto-Delgado, Jorge

    2018-01-01

    A combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular modelling methods were used to understand the potent inhibitory NAD(P)H:quinone oxidoreductase 1 (NQO1) activity of a set of 52 heterocyclic quinones. Molecular docking results indicated that some favourable interactions of key amino acid residues at the binding site of NQO1 with these quinones would be responsible for an improvement of the NQO1 activity of these compounds. The main interactions involved are hydrogen bond of the amino group of residue Tyr128, π-stacking interactions with Phe106 and Phe178, and electrostatic interactions with flavin adenine dinucleotide (FADH) cofactor. Three models were prepared by 3D-QSAR analysis. The models derived from Model I and Model III, shown leave-one-out cross-validation correlation coefficients (q 2 LOO ) of .75 and .73 as well as conventional correlation coefficients (R 2 ) of .93 and .95, respectively. In addition, the external predictive abilities of these models were evaluated using a test set, producing the predicted correlation coefficients (r 2 pred ) of .76 and .74, respectively. The good concordance between the docking results and 3D-QSAR contour maps provides helpful information about a rational modification of new molecules based in quinone scaffold, in order to design more potent NQO1 inhibitors, which would exhibit highly potent antitumor activity. © 2017 John Wiley & Sons A/S.

  12. The importance of data curation on QSAR Modeling ...

    EPA Pesticide Factsheets

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These modern tools allow for more advanced techniques in terms of chemical structure representation and storage, as well as enabling automated data-mining and standardization approaches to examine and fix data quality issues.This presentation will investigate the impact of data curation on the reliability of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. As part of this work we have attempted to disentangle the influence of the quality versus quantity of data based on the Syracuse PHYSPROP database partly used by EPISuite software. We will review our automated approaches to examining key datasets related to the EPISuite data to validate across chemical structure representations (e.g., mol file and SMILES) and identifiers (chemical names and registry numbers) and approaches to standardize data into QSAR-ready formats prior to modeling procedures. Our efforts to quantify and segregate data into quality categories has allowed us to evaluate the resulting models that can be developed from these data slices and to quantify to what extent efforts developing high-quality datasets have the expected pay-off in terms of predicting performance. The most accur

  13. QSAR Study of p56lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

    PubMed Central

    Fassihi, Afshin; Sabet, Razieh

    2008-01-01

    Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously. PMID:19325836

  14. Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical.

    PubMed

    Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard

    2015-11-17

    The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

  15. Reduced density gradient as a novel approach for estimating QSAR descriptors, and its application to 1, 4-dihydropyridine derivatives with potential antihypertensive effects.

    PubMed

    Jardínez, Christiaan; Vela, Alberto; Cruz-Borbolla, Julián; Alvarez-Mendez, Rodrigo J; Alvarado-Rodríguez, José G

    2016-12-01

    The relationship between the chemical structure and biological activity (log IC 50 ) of 40 derivatives of 1,4-dihydropyridines (DHPs) was studied using density functional theory (DFT) and multiple linear regression analysis methods. With the aim of improving the quantitative structure-activity relationship (QSAR) model, the reduced density gradient s( r) of the optimized equilibrium geometries was used as a descriptor to include weak non-covalent interactions. The QSAR model highlights the correlation between the log IC 50 with highest molecular orbital energy (E HOMO ), molecular volume (V), partition coefficient (log P), non-covalent interactions NCI(H4-G) and the dual descriptor [Δf(r)]. The model yielded values of R 2 =79.57 and Q 2 =69.67 that were validated with the next four internal analytical validations DK=0.076, DQ=-0.006, R P =0.056, and R N =0.000, and the external validation Q 2 boot =64.26. The QSAR model found can be used to estimate biological activity with high reliability in new compounds based on a DHP series. Graphical abstract The good correlation between the log IC 50 with the NCI (H4-G) estimated by the reduced density gradient approach of the DHP derivatives.

  16. Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

    PubMed

    Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

    2018-01-01

    The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis.

    PubMed

    Castillo-Garit, Juan Alberto; Abad, Concepción; Rodríguez-Borges, J Enrique; Marrero-Ponce, Yovani; Torrens, Francisco

    2012-01-01

    The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

  18. Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

    NASA Astrophysics Data System (ADS)

    Tan, Wen; Mei, Hu; Chao, Li; Liu, Tengfei; Pan, Xianchao; Shu, Mao; Yang, Li

    2013-12-01

    P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

  19. Molecular Determinants of Juvenile Hormone Action as Revealed by 3D QSAR Analysis in Drosophila

    PubMed Central

    Beňo, Milan; Farkaš, Robert

    2009-01-01

    Background Postembryonic development, including metamorphosis, of many animals is under control of hormones. In Drosophila and other insects these developmental transitions are regulated by the coordinate action of two principal hormones, the steroid ecdysone and the sesquiterpenoid juvenile hormone (JH). While the mode of ecdysone action is relatively well understood, the molecular mode of JH action remains elusive. Methodology/Principal Findings To gain more insights into the molecular mechanism of JH action, we have tested the biological activity of 86 structurally diverse JH agonists in Drosophila melanogaster. The results were evaluated using 3D QSAR analyses involving CoMFA and CoMSIA procedures. Using this approach we have generated both computer-aided and species-specific pharmacophore fingerprints of JH and its agonists, which revealed that the most active compounds must possess an electronegative atom (oxygen or nitrogen) at both ends of the molecule. When either of these electronegative atoms are replaced by carbon or the distance between them is shorter than 11.5 Å or longer than 13.5 Å, their biological activity is dramatically decreased. The presence of an electron-deficient moiety in the middle of the JH agonist is also essential for high activity. Conclusions/Significance The information from 3D QSAR provides guidelines and mechanistic scope for identification of steric and electrostatic properties as well as donor and acceptor hydrogen-bonding that are important features of the ligand-binding cavity of a JH target protein. In order to refine the pharmacophore analysis and evaluate the outcomes of the CoMFA and CoMSIA study we used pseudoreceptor modeling software PrGen to generate a putative binding site surrogate that is composed of eight amino acid residues corresponding to the defined molecular interactions. PMID:19547707

  20. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, wemore » found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R{sup 2} = 0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q{sup 2}{sub ext} = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between

  1. DAT/SERT Selectivity of Flexible GBR 12909 Analogs Modeled Using 3D-QSAR Methods

    PubMed Central

    Gilbert, Kathleen M.; Boos, Terrence L.; Dersch, Christina M.; Greiner, Elisabeth; Jacobson, Arthur E.; Lewis, David; Matecka, Dorota; Prisinzano, Thomas E.; Zhang, Ying; Rothman, Richard B.; Rice, Kenner C.; Venanzi, Carol A.

    2007-01-01

    The dopamine reuptake inhibitor GBR 12909 (1-{2-[bis(4-fluorophenyl)methoxy]ethyl}-4-(3-phenylpropyl)piperazine, 1) and its analogs have been developed as tools to test the hypothesis that selective dopamine transporter (DAT) inhibitors will be useful therapeutics for cocaine addiction. This 3D-QSAR study focuses on the effect of substitutions in the phenylpropyl region of 1. CoMFA and CoMSIA techniques were used to determine a predictive and stable model for the DAT/serotonin transporter (SERT) selectivity (represented by pKi (DAT/SERT)) of a set of flexible analogs of 1, most of which have eight rotatable bonds. In the absence of a rigid analog to use as a 3D-QSAR template, six conformational families of analogs were constructed from six pairs of piperazine and piperidine template conformers identified by hierarchical clustering as representative molecular conformations. Three models stable to y-value scrambling were identified after a comprehensive CoMFA and CoMSIA survey with Region Focusing. Test set correlation validation led to an acceptable model, with q2 = 0.508, standard error of prediction = 0.601, two components, r2 = 0.685, standard error of estimate = 0.481, F value = 39, percent steric contribution = 65, and percent electrostatic contribution = 35. A CoMFA contour map identified areas of the molecule that affect pKi (DAT/SERT). This work outlines a protocol for deriving a stable and predictive model of the biological activity of a set of very flexible molecules. PMID:17127069

  2. [Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

    PubMed

    Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

    2013-01-01

    QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

  3. 3D-QSAR studies on 1,2,4-triazolyl 5-azaspiro [2.4]-heptanes as D3R antagonists

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Zhang, Hui

    2018-07-01

    Dopamine D3 receptor has become an attractive target in the treatment of abused drugs. 3D-QSAR studies were performed on a novel series of D3 receptor antagonists, 1,2,4-triazolyl 5-azaspiro [2.4]-heptanes, using CoMFA and CoMSIA methods. Two predictive 3D-QSAR models have been generated for the modified design of D3R antagonists. Based on the steric, electrostatic, hydrophobic and hydrogen-bond acceptor information of contour maps, key structural factors affecting the bioactivity were explored. This work gives helpful suggestions on the design of novel D3R antagonists with increased activities.

  4. Validity and validation of expert (Q)SAR systems.

    PubMed

    Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

    2005-08-01

    At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

  5. Development of a QSAR Model for Thyroperoxidase Inhbition ...

    EPA Pesticide Factsheets

    hyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the cross- and external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre-registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to

  6. Molecular docking and 3D-QSAR studies on triazolinone and pyridazinone, non-nucleoside inhibitor of HIV-1 reverse transcriptase.

    PubMed

    Sivan, Sree Kanth; Manga, Vijjulatha

    2010-06-01

    Nonnucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors of the HIV-1 reverse transcriptase. Recently a series of Triazolinone and Pyridazinone were reported as potent inhibitors of HIV-1 wild type reverse transcriptase. In the present study, docking and 3D quantitative structure activity relationship (3D QSAR) studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 31 molecules. Ligands were built and minimized using Tripos force field and applying Gasteiger-Hückel charges. These ligands were docked into protein active site using GLIDE 4.0. The docked poses were analyzed; the best docked poses were selected and aligned. CoMFA and CoMSIA fields were calculated using SYBYL6.9. The molecules were divided into training set and test set, a PLS analysis was performed and QSAR models were generated. The model showed good statistical reliability which is evident from the r2 nv, q2 loo and r2 pred values. The CoMFA model provides the most significant correlation of steric and electrostatic fields with biological activities. The CoMSIA model provides a correlation of steric, electrostatic, acceptor and hydrophobic fields with biological activities. The information rendered by 3D QSAR model initiated us to optimize the lead and design new potential inhibitors.

  7. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    PubMed

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

  8. SEDIMENT-ASSOCIATED REACTIONS OF AROMATIC AMINES: QSAR DEVELOPMENT

    EPA Science Inventory

    Despite the common occurrence of the aromatic amine functional group in environmental contaminants, few quantitative structure-activity relationships (QSARs) have been developed to predict sorption kinetics for aromatic amines in natural soils and sediments. Towards the goal of d...

  9. Using Toxicological Evidence from QSAR Models in Practice

    EPA Science Inventory

    The new generation of QSAR models provides supporting documentation in addition to the predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances and to review and increase the reliability of toxicity predictions. Thi...

  10. Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

    PubMed

    Ebalunode, Jerry O; Zheng, Weifan; Tropsha, Alexander

    2011-01-01

    Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

  11. Are the Chemical Structures in your QSAR Correct?

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumptio...

  12. Azolium analogues as CDK4 inhibitors: Pharmacophore modeling, 3D QSAR study and new lead drug discovery

    NASA Astrophysics Data System (ADS)

    Rondla, Rohini; Padma Rao, Lavanya Souda; Ramatenki, Vishwanath; Vadija, Rajender; Mukkera, Thirupathi; Potlapally, Sarita Rajender; Vuruputuri, Uma

    2017-04-01

    The cyclin-dependent kinase 4 (CDK4) enzyme is a key regulator in cell cycle G1 phase progression. It is often overexpressed in variety of cancer cells, which makes it an attractive therapeutic target for cancer treatment. A number of chemical scaffolds have been reported as CDK4 inhibitors in the literature, and in particular azolium scaffolds as potential inhibitors. Here, a ligand based pharmacophore modeling and an atom based 3D-QSAR analyses for a series of azolium based CDK4 inhibitors are presented. A five point pharmacophore hypothesis, i.e. APRRR with one H-bond acceptor (A), one positive cationic feature (P) and three ring aromatic sites (R) is developed, which yielded an atom based 3D-QSAR model that shows an excellent correlation coefficient value- R2 = 0.93, fisher ratio- F = 207, along with good predictive ability- Q2 = 0.79, and Pearson R value = 0.89. The visual inspection of the 3D-QSAR model, with the most active and the least active ligands, demonstrates the favorable and unfavorable structural regions for the activity towards CDK4. The roles of positively charged nitrogen, the steric effect, ligand flexibility, and the substituents on the activity are in good agreement with the previously reported experimental results. The generated 3D QSAR model is further applied as query for a 3D database screening, which identifies 23 lead drug candidates with good predicted activities and diverse scaffolds. The ADME analysis reveals that, the pharmacokinetic parameters of all the identified new leads are within the acceptable range.

  13. QSAR analysis of nicotinamidic compounds and design of potential Bruton's tyrosine kinase (Btk) inhibitors.

    PubMed

    Santos-Garcia, Letícia; Assis, Letícia C; Silva, Daniela R; Ramalho, Teodorico C; da Cunha, Elaine F F

    2016-07-01

    Bruton's tyrosine kinase (Btk) is an important enzyme in B-lymphocyte development and differentiation. Furthermore, Btk expression is considered essential for the proliferation and survival of these cells. Btk inhibition has become an attractive strategy for treating autoimmune diseases, B-cell leukemia, and lymphomas. With the objective of proposing new candidates for Btk inhibitors, we applied receptor-dependent four-dimensional quantitative structure-activity relationship (QSAR) methodology to a series of 96 nicotinamide analogs useful as Btk modulators. The QSAR models were developed using 71 compounds, the training set, and externally validated using 25 compounds, the test set. The conformations obtained by molecular dynamics simulation were overlapped in a virtual three-dimensional cubic box comprised of 2 and 5 Å cells, according to the six trial alignments. The models were generated by combining genetic function approximation and partial least squares regression technique. The analyses suggest that Model 1a yields the best results. The best equation shows [Formula: see text], r(2) = .743, RMSEC = .831, RMSECV = .879. Given the importance of the Tyr551, this residue could become a strategic target for the design of novel Btk inhibitors with improved potency. In addition, the good potency predicted for the proposed M2 compound indicates this compound as a potential Btk inhibitor candidate.

  14. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

    PubMed Central

    Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

    2015-01-01

    The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. PMID:26110383

  15. Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1).

    PubMed

    Begum, S; Achary, P Ganga Raju

    2015-01-01

    Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.

  16. Molecular Docking, Pharmacophore, and 3D-QSAR Approach: Can Adenine Derivatives Exhibit Significant Inhibitor Towards Ebola Virus?

    PubMed Central

    Rai, Amit; Aboumanei, Mohamed H.; Verma, Suraj P.; Kumar, Sachidanand; Raj, Vinit

    2017-01-01

    Introduction: Ebola Virus Disease (EVD) is caused by Ebola virus, which is often accompanied by fatal hemorrhagic fever upon infection in humans. This virus has caused the majority of deaths in human. There are no proper vaccinations and medications available for EVD. It is pivoting the attraction of scientist to develop the potent vaccination or novel lead to inhibit Ebola virus. Methods & Materials: In the present study, we developed 3D-QSAR and the pharmacophoric model from the previous reported potent compounds for the Ebola virus. Results & Discussion: Results & Discussion: The pharmacophoric model AAAP.116 was generated with better survival value and selectivity. Moreover, the 3D-QSAR model also showed the best r2 value 0.99 using PLS factor. Thereby, we found the higher F value, which demonstrated the statistical significance of both the models. Furthermore, homological modeling and molecular docking study were performed to analyze the affinity of the potent lead. This showed the best binding energy and bond formation with targeted protein. Conclusion: Finally, all the results of this study concluded that 3D-QSAR and Pharmacophore models may be helpful to search potent lead for EVD treatment in future. PMID:29387271

  17. 4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

    PubMed Central

    Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

    2008-01-01

    Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

  18. QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity.

    PubMed

    Sousa, Inês J; Ferreira, Maria-José U; Molnár, Joseph; Fernandes, Miguel X

    2013-02-14

    Multidrug resistance (MDR) represents a major limitation for cancer chemotherapy. There are several mechanisms of MDR but the most important is associated with P-glycoprotein (P-gp) overexpression. The development of modulators of P-gp that are able to re-establish drug sensitivity of resistant cells has been considered a promising approach for overcoming MDR. Macrocyclic lathyrane and jatrophane-type diterpenes from Euphorbia species were found to be strong MDR reversing agents. In this study we applied quantitative structure-activity relationship (QSAR) methodology in order to identify the most relevant molecular features of macrocyclic diterpenes with P-gp inhibitory activity and to determine which structural modifications can be performed to improve their activity. Using experimental biological data at two concentrations (4 and 40 μg/ml), we developed a QSAR model for a set of 51 bioactive diterpenic compounds which includes lathyrane and jatrophane-type diterpenes and another model just for jatrophanes. The cross-validation correlation values for all diterpenes QSAR models developed for biological activities at compound concentrations of 4 and 40 μg/ml were 0.758 and 0.729, respectively. Regarding the prediction ability, we get R²(pred) values of 0.765 and 0.534 for biological activities at compound concentrations of 4 and 40 μg/ml, respectively. Applying the cross-validation test to jatrophanes QSAR models, we obtained 0.680 and 0.787 for biological activities at compound concentrations of 4 and 40 μg/ml concentrations, respectively. For the same concentrations, the obtained R²(pred) values for jatrophanes models were 0.541 and 0.534, respectively. The obtained models were statistically valid and showed high prediction ability. Copyright © 2012 Elsevier B.V. All rights reserved.

  19. 4D-Qsar Study of Some Pyrazole Pyridine Carboxylic Acid Derivatives by Electron Conformational-Genetic Algorithm Method.

    PubMed

    Tuzun, Burak; Yavuz, Sevtap Caglar; Sabanci, Nazmiye; Saripinar, Emin

    2018-05-13

    In the present work, pharmacophore identification and biological activity prediction for 86 pyrazole pyridine carboxylic acid derivatives were made using the electron conformational genetic algorithm approach which was introduced as a 4D-QSAR analysis by us in recent years. In the light of the data obtained from quantum chemical calculations at HF/6-311 G** level, the electron conformational matrices of congruity (ECMC) were constructed by EMRE software. Comparing the matrices, electron conformational submatrix of activity (ECSA, Pha) was revealed that are common for these compounds within a minimum tolerance. A parameter pool was generated considering the obtained pharmacophore. To determine the theoretical biological activity of molecules and identify the best subset of variables affecting bioactivities, we used the nonlinear least square regression method and genetic algorithm. The results obtained in this study are in good agreement with the experimental data presented in the literature. The model for training and test sets attained by the optimum 12 parameters gave highly satisfactory results with R2training= 0.889, q2=0.839 and SEtraining=0.066, q2ext1 = 0.770, q2ext2 = 0.750, q2ext3=0.824, ccctr = 0.941, ccctest = 0.869 and cccall = 0.927. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Elucidation of chemosensitization effect of acridones in cancer cell lines: Combined pharmacophore modeling, 3D QSAR, and molecular dynamics studies.

    PubMed

    Gade, Deepak Reddy; Makkapati, Amareswararao; Yarlagadda, Rajesh Babu; Peters, Godefridus J; Sastry, B S; Rajendra Prasad, V V S

    2018-06-01

    Overexpression of P-glycoprotein (P-gp) leads to the emergence of multidrug resistance (MDR) in cancer treatment. Acridones have the potential to reverse MDR and sensitize cells. In the present study, we aimed to elucidate the chemosensitization potential of acridones by employing various molecular modelling techniques. Pharmacophore modeling was performed for the dataset of chemosensitizing acridones earlier proved for cytotoxic activity against MCF7 breast cancer cell line. Gaussian-based QSAR studies also performed to predict the favored and disfavored region of the acridone molecules. Molecular dynamics simulations were performed for compound 10 and human P-glycoprotein (obtained from Homology modeling). An efficient pharmacophore containing 2 hydrogen bond acceptors and 3 aromatic rings (AARRR.14) was identified. NCI 2012 chemical database was screened against AARRR.14 CPH and identified 25 best-fit molecules. Potential regions of the compound were identified through Field (Gaussian) based QSAR. Regression analysis of atom-based QSAR resulted in r 2 of 0.95 and q 2 of 0.72, whereas, regression analysis of field-based QSAR resulted in r 2 of 0.92 and q 2 of 0.87 along with r 2 cv as 0.71. The fate of the acridone molecule (compound 10) in the P-glycoprotein environment is analyzed through analyzing the conformational changes occurring during the molecular dynamics simulations. Combined data of different in silico techniques provided basis for deeper understanding of structural and mechanistic insights of interaction phenomenon of acridones with P-glycoprotein and also as strategic basis for designing more potent molecules for anti-cancer and multidrug resistance reversal activities. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

    PubMed Central

    Scior, Thomas; Lozano-Aponte, Jorge; Ajmani, Subhash; Hernández-Montero, Eduardo; Chávez-Silva, Fabiola; Hernández-Núñez, Emanuel; Moo-Puc, Rosa; Fraguela-Collar, Andres; Navarrete-Vázquez, Gabriel

    2015-01-01

    In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target. PMID:25872791

  2. Pharmacophore Modelling and 4D-QSAR Study of Ruthenium(II) Arene Complexes as Anticancer Agents (Inhibitors) by Electron Conformational- Genetic Algorithm Method.

    PubMed

    Yavuz, Sevtap Caglar; Sabanci, Nazmiye; Saripinar, Emin

    2018-01-01

    The EC-GA method was employed in this study as a 4D-QSAR method, for the identification of the pharmacophore (Pha) of ruthenium(II) arene complex derivatives and quantitative prediction of activity. The arrangement of the computed geometric and electronic parameters for atoms and bonds of each compound occurring in a matrix is known as the electron-conformational matrix of congruity (ECMC). It contains the data from HF/3-21G level calculations. Compounds were represented by a group of conformers for each compound rather than a single conformation, known as fourth dimension to generate the model. ECMCs were compared within a certain range of tolerance values by using the EMRE program and the responsible pharmacophore group for ruthenium(II) arene complex derivatives was found. For selecting the sub-parameter which had the most effect on activity in the series and the calculation of theoretical activity values, the non-linear least square method and genetic algorithm which are included in the EMRE program were used. In addition, compounds were classified as the training and test set and the accuracy of the models was tested by cross-validation statistically. The model for training and test sets attained by the optimum 10 parameters gave highly satisfactory results with R2 training= 0.817, q 2=0.718 and SEtraining=0.066, q2 ext1 = 0.867, q2 ext2 = 0.849, q2 ext3 =0.895, ccctr = 0.895, ccctest = 0.930 and cccall = 0.905. Since there is no 4D-QSAR research on metal based organic complexes in the literature, this study is original and gives a powerful tool to the design of novel and selective ruthenium(II) arene complexes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  3. QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα

    PubMed Central

    Alam, Sarfaraz; Khan, Feroz

    2014-01-01

    Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure–activity relationship represented by the QSAR model yielded a high activity–descriptors relationship accuracy (84%) referred by regression coefficient (r2=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors – dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area – were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets. PMID:24516330

  4. Neural network-based QSAR and insecticide discovery: spinetoram

    NASA Astrophysics Data System (ADS)

    Sparks, Thomas C.; Crouse, Gary D.; Dripps, James E.; Anzeveno, Peter; Martynow, Jacek; DeAmicis, Carl V.; Gifford, James

    2008-06-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry.

  5. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents.

    PubMed

    Speck-Planche, Alejandro; Kleandrova, Valeria V; Luan, Feng; Cordeiro, M Natália D S

    2012-08-01

    The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

    PubMed

    Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

    2013-06-01

    Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

  7. Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach

    PubMed Central

    Bhhatarai, Barun; Wilson, Daniel M.; Price, Paul S.; Marty, Sue; Parks, Amanda K.; Carney, Edward

    2016-01-01

    Background: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. Objectives: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 μM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. Methods: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. Results: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92–100%) and specificity (70–81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 μM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 μM were active in the uterotrophic assay. Conclusions: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had

  8. Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

    NASA Astrophysics Data System (ADS)

    Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

    2008-09-01

    The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement

  9. Investigation of Antigen-Antibody Interactions of Sulfonamides with a Monoclonal Antibody in a Fluorescence Polarization Immunoassay Using 3D-QSAR Models

    PubMed Central

    Wang, Zhanhui; Kai, Zhenpeng; Beier, Ross C.; Shen, Jianzhong; Yang, Xinling

    2012-01-01

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAbSMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The affinities of the MAbSMR, expressed as Log10IC50, for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). The results demonstrated that the proposed pharmacophore model containing two hydrogen-bond acceptors, two hydrogen-bond donors and two hydrophobic centers characterized the structural features of the sulfonamides necessary for MAbSMR binding. Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR models of 15 sulfonamides based on CoMFA and CoMSIA resulted in q2 cv values of 0.600 and 0.523, and r2 values of 0.995 and 0.994, respectively, which indicates that both methods have significant predictive capability. Connolly surface analysis, which mainly focused on steric force fields, was performed to complement the results from CoMFA and CoMSIA. This novel study combining FPIA with pharmacophore modeling demonstrates that multidisciplinary research is useful for investigating antigen-antibody interactions and also may provide information required for the design of new haptens. PMID:22754368

  10. QSAR Study on the anti-tumor activity of levofloxacin-thiadiazole HDACi conjugates

    NASA Astrophysics Data System (ADS)

    Tang, Ziqiang; Feng, Hui; Chen, Yan; Yue, Wei; Feng, Changjun

    2017-12-01

    A molecular electronegativity distance vector(M t) based on 13atomic types is used to describe the structures of 19 conjugates(LHCc) of levofloxacin-thiadiazole HDAC inhibitor(HDACi) and related to the anti-tumor activity (M F and P C) of LHCc against MCF-7 and PC-3. The quantitative structure-activity relationships (QSAR) was established by using leaps-and-bounds regression analysis for the anti-tumor activities (M F and P C) of 19 above compounds to MCF-7and PC-3 along with the M t. The correlation coefficients (R 2) and the leave-one-out (LOO) cross validation R cv 2 for the M F and P C models were 0.792 and 0.679; 0.773 and 0.565, respectively. The QSAR models have favorable correlation, as well as robustness and good prediction capability by R 2, F, R cv 2, A IC F IT V IF tests. The results indicate that the molecular structural units: -CHg-(g=1, 2), -NH2, -NH-,-OH, O=, -O-, -S- and -X are main factors which can affect the anti-tumor activity M F and PC bioactivities of these compounds directly.

  11. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    PubMed

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  12. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    PubMed Central

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2015-01-01

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. PMID:25560673

  13. Combined 3D-QSAR modeling and molecular docking study on azacycles CCR5 antagonists

    NASA Astrophysics Data System (ADS)

    Ji, Yongjun; Shu, Mao; Lin, Yong; Wang, Yuanqiang; Wang, Rui; Hu, Yong; Lin, Zhihua

    2013-08-01

    The beta chemokine receptor 5 (CCR5) is an attractive target for pharmaceutical industry in the HIV-1, inflammation and cancer therapeutic areas. In this study, we have developed quantitative structure activity relationship (QSAR) models for a series of 41 azacycles CCR5 antagonists using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA methods. The cross-validated coefficient q2 values of 3D-QASR (CoMFA, CoMSIA, and Topomer CoMFA) methods were 0.630, 0.758, and 0.852, respectively, the non-cross-validated R2 values were 0.979, 0.978, and 0.990, respectively. Docking studies were also employed to determine the most probable binding mode. 3D contour maps and docking results suggested that bulky groups and electron-withdrawing groups on the core part would decrease antiviral activity. Furthermore, docking results indicated that H-bonds and π bonds were favorable for antiviral activities. Finally, a set of novel derivatives with predicted activities were designed.

  14. Efficient dynamic molecular simulation using QSAR model to know inhibition activity in breast cancer medicine

    NASA Astrophysics Data System (ADS)

    Zharifah, A.; Kusumowardani, E.; Saputro, A.; Sarwinda, D.

    2017-07-01

    According to data from GLOBOCAN (IARC) at 2012, breast cancer was the highest rated of new cancer case by 43.3 % (after controlled by age), with mortality rated as high as 12.9 %. Oncology is a major field which focusing on improving the development of drug and therapeutics cancer in pharmaceutical and biotechnology companies. Nowadays, many researchers lead to computational chemistry and bioinformatic for pharmacophore generation. A pharmacophore describes as a group of atoms in the molecule which is considered to be responsible for a pharmacological action. Prediction of biological function from chemical structure in silico modeling reduces the use of chemical reagents so the risk of environmental pollution decreased. In this research, we proposed QSAR model to analyze the composition of cancer drugs which assumed to be homogenous in character and treatment. Atomic interactions which analyzed are learned through parameters such as log p as descriptors hydrophobic, n_poinas descriptor contour strength and molecular structure, and also various concentrations inhibitor (micromolar and nanomolar) from NCBI drugs bank. The differences inhibitor activity was observed by the presence of IC 50 residues value from inhibitor substances at various concentration. Then, we got a general overview of the state of safety for drug stability seen from its IC 50 value. In our study, we also compared between micromolar and nanomolar inhibitor effect from QSAR model results. The QSAR model analysis shows that the drug concentration with nanomolar is better than micromolar, related with the content of inhibitor substances concentration. This QSAR model got the equation: Log 1/IC50 = (0.284) (±0.195) logP + (0.02) (±0.012) n_poin + (-0.005) (±0.083) Inhibition10.2nanoM + (0.1) (±0.079) Inhibition30.5nanoM + (-0.016) (±0.045) Inhibition91.5nanoM + (-2.572) (±1.570) (n = 13; r = 0.813; r2 = 0.660; s = 0.764; F = 2.720; q2 = 0.660).

  15. QSAR, QSPR and QSRR in Terms of 3-D-MoRSE Descriptors for In Silico Screening of Clofibric Acid Analogues.

    PubMed

    Di Tullio, Maurizio; Maccallini, Cristina; Ammazzalorso, Alessandra; Giampietro, Letizia; Amoroso, Rosa; De Filippis, Barbara; Fantacuzzi, Marialuigia; Wiczling, Paweł; Kaliszan, Roman

    2012-07-01

    A series of 27 analogues of clofibric acid, mostly heteroarylalkanoic derivatives, have been analyzed by a novel high-throughput reversed-phase HPLC method employing combined gradient of eluent's pH and organic modifier content. The such determined hydrophobicity (lipophilicity) parameters, log kw , and acidity constants, pKa , were subjected to multiple regression analysis to get a QSRR (Quantitative StructureRetention Relationships) and a QSPR (Quantitative Structure-Property Relationships) equation, respectively, describing these pharmacokinetics-determining physicochemical parameters in terms of the calculation chemistry derived structural descriptors. The previously determined in vitro log EC50 values - transactivation activity towards PPARα (human Peroxisome Proliferator-Activated Receptor α) - have also been described in a QSAR (Quantitative StructureActivity Relationships) equation in terms of the 3-D-MoRSE descriptors (3D-Molecule Representation of Structures based on Electron diffraction descriptors). The QSAR model derived can serve for an a priori prediction of bioactivity in vitro of any designed analogue, whereas the QSRR and the QSPR models can be used to evaluate lipophilicity and acidity, respectively, of the compounds, and hence to rational guide selection of structures of proper pharmacokinetics. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

    PubMed

    Pérez-Garrido, Alfonso; Morales Helguera, Aliuska; Abellán Guillén, Adela; Cordeiro, M Natália D S; Garrido Escudero, Amalio

    2009-01-15

    This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

  17. Synthesis and quantitative structure activity relationship (QSAR) of arylidene (benzimidazol-1-yl)acetohydrazones as potential antibacterial agents.

    PubMed

    El-Kilany, Yeldez; Nahas, Nariman M; Al-Ghamdi, Mariam A; Badawy, Mohamed E I; El Ashry, El Sayed H

    2015-01-01

    Ethyl (benzimidazol-1-yl)acetate was subjected to hydrazinolysis with hydrazine hydrate to give (benzimidazol-1-yl)acetohydrazide. The latter was reacted with various aromatic aldehydes to give the respective arylidene (1H-benzimidazol-1-yl)acetohydrazones. Solutions of the prepared hydrazones were found to contain two geometric isomers. Similarly (2-methyl-benzimidazol-1-yl)acetohydrazide was reacted with various aldehydes to give the corresponding hydrazones. The antibacterial activity was evaluated in vitro by minimum inhibitory concentration (MIC) against Agrobacterium tumefaciens (A. tumefaciens), Erwinia carotovora (E. carotovora), Corynebacterium fascians (C. fascians) and Pseudomonas solanacearum (P. solanacearum). MIC result demonstrated that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (4) was the most active compound (MIC = 20, 35, 25 and 30 mg/L against A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively). Quantitative structure activity relationship (QSAR) investigation using Hansch analysis was applied to find out the correlation between antibacterial activity and physicochemical properties. Various physicochemical descriptors and experimentally determined MIC values for different microorganisms were used as independent and dependent variables, respectively. pMICs of the compounds exhibited good correlation (r = 0.983, 0.914, 0.960 and 0.958 for A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively) with the prediction made by the model. QSAR study revealed that the hydrophobic parameter (ClogP), the aqueous solubility (LogS), calculated molar refractivity, topological polar surface area and hydrogen bond acceptor were found to have overall significant correlation with antibacterial activity. The statistical results of training set, correlation coefficient (r and r (2)), the ratio between regression and residual variances (f, Fisher's statistic), the standard error of estimates and

  18. Structural exploration for the refinement of anticancer matrix metalloproteinase-2 inhibitor designing approaches through robust validated multi-QSARs

    NASA Astrophysics Data System (ADS)

    Adhikari, Nilanjan; Amin, Sk. Abdul; Saha, Achintya; Jha, Tarun

    2018-03-01

    Matrix metalloproteinase-2 (MMP-2) is a promising pharmacological target for designing potential anticancer drugs. MMP-2 plays critical functions in apoptosis by cleaving the DNA repair enzyme namely poly (ADP-ribose) polymerase (PARP). Moreover, MMP-2 expression triggers the vascular endothelial growth factor (VEGF) having a positive influence on tumor size, invasion, and angiogenesis. Therefore, it is an urgent need to develop potential MMP-2 inhibitors without any toxicity but better pharmacokinetic property. In this article, robust validated multi-quantitative structure-activity relationship (QSAR) modeling approaches were attempted on a dataset of 222 MMP-2 inhibitors to explore the important structural and pharmacophoric requirements for higher MMP-2 inhibition. Different validated regression and classification-based QSARs, pharmacophore mapping and 3D-QSAR techniques were performed. These results were challenged and subjected to further validation to explain 24 in house MMP-2 inhibitors to judge the reliability of these models further. All these models were individually validated internally as well as externally and were supported and validated by each other. These results were further justified by molecular docking analysis. Modeling techniques adopted here not only helps to explore the necessary structural and pharmacophoric requirements but also for the overall validation and refinement techniques for designing potential MMP-2 inhibitors.

  19. Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

    PubMed Central

    Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the

  20. Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients.

    PubMed

    Bauer, Katharina Christin; Hämmerling, Frank; Kittelmann, Jörg; Dürr, Cathrin; Görlich, Fabian; Hubbuch, Jürgen

    2017-04-01

    Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  1. 3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors

    PubMed Central

    Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

    2016-01-01

    Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q2) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

  2. QSAR development and bioavailability determination: the toxicity of chloroanilines to the soil dwelling springtail Folsomia candida.

    PubMed

    Giesen, Daniel; van Gestel, Cornelis A M

    2013-03-01

    Quantitative structure-activity relationships (QSARs) are an established tool in environmental risk assessment and a valuable alternative to the exhaustive use of test animals under REACH. In this study a QSAR was developed for the toxicity of a series of six chloroanilines to the soil-dwelling collembolan Folsomia candida in standardized natural LUFA2.2 soil. Toxicity endpoints incorporated in the QSAR were the concentrations causing 10% (EC10) and 50% (EC50) reduction in reproduction of F. candida. Toxicity was based on concentrations in interstitial water estimated from nominal concentrations in the soil and published soil-water partition coefficients. Estimated effect concentrations were negatively correlated with the lipophilicity of the compounds. Interstitial water concentrations for both the EC10 and EC50 for four compounds were determined by using solid-phase microextraction (SPME). Measured and estimated concentrations were comparable only for tetra- and pentachloroaniline. With decreasing chlorination the disparity between modelled and actual concentrations increased. Optimisation of the QSAR therefore could not be accomplished, showing the necessity to move from total soil to (bio)available concentration measurements. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Multiple QSAR models, pharmacophore pattern and molecular docking analysis for anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues

    NASA Astrophysics Data System (ADS)

    Masand, Vijay H.; El-Sayed, Nahed N. E.; Bambole, Mukesh U.; Quazi, Syed A.

    2018-04-01

    Multiple discrete quantitative structure-activity relationships (QSARs) models were constructed for the anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues with a variety of substituents like sbnd Br, sbnd OH, -OMe, etc. at different positions. A big pool of descriptors was considered for QSAR model building. Genetic algorithm (GA), available in QSARINS-Chem, was executed to choose optimum number and set of descriptors to create the multi-linear regression equations for a dataset of sixty-nine compounds. The newly developed five parametric models were subjected to exhaustive internal and external validation along with Y-scrambling using QSARINS-Chem, according to the OECD principles for QSAR model validation. The models were built using easily interpretable descriptors and accepted after confirming statistically robustness with high external predictive ability. The five parametric models were found to have R2 = 0.80 to 0.86, R2ex = 0.75 to 0.84, and CCCex = 0.85 to 0.90. The models indicate that frequency of nitrogen and oxygen atoms separated by five bonds from each other and internal electronic environment of the molecule have correlation with the anticancer activity.

  4. Determination and importance of temperature dependence of retention coefficient (RPHPLC) in QSAR model of nitrazepams' partition coefficient in bile acid micelles.

    PubMed

    Posa, Mihalj; Pilipović, Ana; Lalić, Mladena; Popović, Jovan

    2011-02-15

    Linear dependence between temperature (t) and retention coefficient (k, reversed phase HPLC) of bile acids is obtained. Parameters (a, intercept and b, slope) of the linear function k=f(t) highly correlate with bile acids' structures. Investigated bile acids form linear congeneric groups on a principal component (calculated from k=f(t)) score plot that are in accordance with conformations of the hydroxyl and oxo groups in a bile acid steroid skeleton. Partition coefficient (K(p)) of nitrazepam in bile acids' micelles is investigated. Nitrazepam molecules incorporated in micelles show modified bioavailability (depo effect, higher permeability, etc.). Using multiple linear regression method QSAR models of nitrazepams' partition coefficient, K(p) are derived on the temperatures of 25°C and 37°C. For deriving linear regression models on both temperatures experimentally obtained lipophilicity parameters are included (PC1 from data k=f(t)) and in silico descriptors of the shape of a molecule while on the higher temperature molecular polarisation is introduced. This indicates the fact that the incorporation mechanism of nitrazepam in BA micelles changes on the higher temperatures. QSAR models are derived using partial least squares method as well. Experimental parameters k=f(t) are shown to be significant predictive variables. Both QSAR models are validated using cross validation and internal validation method. PLS models have slightly higher predictive capability than MLR models. Copyright © 2010 Elsevier B.V. All rights reserved.

  5. Investigation of antigen-antibody interactions of sulfonamides with a monoclonal antibody in a fluorescence polarization immunoassay using 3D-QSAR models

    USDA-ARS?s Scientific Manuscript database

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAbSMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular si...

  6. QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.

    PubMed

    Gramatica, Paola; Papa, Ester; Sangion, Alessandro

    2018-01-24

    The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

  7. Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model.

    PubMed

    Kim, J; Lee, C; Chong, Y

    2009-01-01

    Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q(2) = 0.763, r(2) = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.

  8. 3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles

    NASA Astrophysics Data System (ADS)

    Ghaleb, Adib; Aouidate, Adnane; Ghamali, Mounir; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

    2017-10-01

    3D-QSAR (comparative molecular field analysis (CoMFA)) and comparative molecular similarity indices analysis (CoMSIA) were performed on novel 2,5 disubstituted 1,3,4-oxadiazoles analogues as anti-fungal agents. The CoMFA and CoMSIA models using 13 compounds in the training set gives Q2 values of 0.52 and 0.51 respectively, while R2 values of 0.92. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to determine a three-dimensional quantitative structure-activity relationship. Based on this study a set of new molecules with high predicted activities were designed. Surflex-docking confirmed the stability of predicted molecules in the receptor.

  9. QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    PubMed Central

    Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander

    2009-01-01

    Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371

  10. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

    PubMed

    Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

    2017-06-30

    Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

  11. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

    PubMed Central

    2017-01-01

    Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

  12. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox.

  13. Design, synthesis, pharmacological evaluation and in silico ADMET prediction of novel substituted benzimidazole derivatives as angiotensin II-AT1 receptor antagonists based on predictive 3D QSAR models.

    PubMed

    Vyas, V K; Gupta, N; Ghate, M; Patel, S

    2014-01-01

    In this study we designed novel substituted benzimidazole derivatives and predicted their absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, based on a predictive 3D QSAR study on 132 substituted benzimidazoles as AngII-AT1 receptor antagonists. The two best predicted compounds were synthesized and evaluated for AngII-AT1 receptor antagonism. Three different alignment tools for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used. The best 3D QSAR models were obtained using the rigid body (Distill) alignment method. CoMFA and CoMSIA models were found to be statistically significant with leave-one-out correlation coefficients (q(2)) of 0.630 and 0.623, respectively, cross-validated coefficients (r(2)cv) of 0.651 and 0.630, respectively, and conventional coefficients of determination (r(2)) of 0.848 and 0.843, respectively. 3D QSAR models were validated using a test set of 24 compounds, giving satisfactory predicted results (r(2)pred) of 0.727 and 0.689 for the CoMFA and CoMSIA models, respectively. We have identified some key features in substituted benzimidazole derivatives, such as lipophilicity and H-bonding at the 2- and 5-positions of the benzimidazole nucleus, respectively, for AT1 receptor antagonistic activity. We designed 20 novel substituted benzimidazole derivatives and predicted their activity. In silico ADMET properties were also predicted for these designed molecules. Finally, the compounds with best predicted activity were synthesized and evaluated for in vitro angiotensin II-AT1 receptor antagonism.

  14. Docking Analysis and Multidimensional Hybrid QSAR Model of 1,4-Benzodiazepine-2,5-Diones as HDM2 Antagonists.

    PubMed

    Dai, Yujie; Chen, Nan; Wang, Qiang; Zheng, Heng; Zhang, Xiuli; Jia, Shiru; Dong, Lilong; Feng, Dacheng

    2012-01-01

    The inhibitors of p53-HDM2 interaction are attractive molecules for the treatment of wild-type p53 tumors. In order to search more potent HDM2 inhibitors, docking operation with CDOCKER protocol in Discovery Studio 2.1 (DS2.1) and multidimensional hybrid quantitative structure-activity relationship (QSAR) studies through the physiochemical properties obtained from DS2.1 and E-Dragon 1.0 as descriptors, have been performed on 59 1,4-benzodiazepine- 2,5-diones which have p53-HDM2 interaction inhibitory activities. The docking results indicate that π-π interaction between the imidazole group in HIS96 and the aryl ring at 4-N of 1,4-benzodiazepine-2,5-dione may be one of the key factors for the combination of ligands with HDM2. Two QSAR models were obtained using genetic function approximation (GFA) and genetic partial least squares (G/PLS) based on the descriptors obtained from DS2.1 and E-dragon 1.0, respectively. The best model can explain 85.5% of the variance (R (2) adj ) while it could predict 81.7% of the variance (R (2) cv ). With this model, the bioactivities of some new compounds were predicted.

  15. SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS

    EPA Science Inventory

    Abstract

    SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS

    Risk assessment typically incorporates some relevant toxicity information upon which to base a sound estimation for a chemical of concern. However, there are many circumstances in whic...

  16. AQUATIC TOXICITY MODE OF ACTION STUDIES APPLIED TO QSAR DEVELOPMENT

    EPA Science Inventory

    A series of QSAR models for predicting fish acute lethality were developed using systematically collected data on more than 600 chemicals. These models were developed based on the assumption that chemicals producing toxicity through a common mechanism will have commonality in the...

  17. An examination of data quality on QSAR Modeling in regards ...

    EPA Pesticide Factsheets

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to develop and validate QSAR models. We have focused our efforts on the widely used EPISuite software that was initially developed over two decades ago and, specifically, on the PHYSPROP dataset used to train the EPISuite prediction models. This presentation will review our approaches to examining key datasets, the delivery of curated data and the development of machine-learning models for thirteen separate property endpoints of interest to environmental science. We will also review how these data will be made freely accessible to the community via a new “chemistry dashboard”. This abstract does not reflect U.S. EPA policy. presentation at UNC-CH.

  18. QSAR studies on carbonic anhydrase inhibitors: a case of ureido and thioureido derivatives of aromatic/heterocyclic sulfonamides.

    PubMed

    Agrawal, Vijay K; Sharma, Ruchi; Khadikar, Padmakar V

    2002-09-01

    QSAR studies on modelling of biological activity (hCAI) for a series of ureido and thioureido derivatives of aromatic/heterocyclic sulfonamides have been made using a pool of topological indices. Regression analysis of the data showed that excellent results were obtained in multiparametric correlations upon introduction of indicator parameters. The predictive abilities of the models are discussed using cross-validation parameters.

  19. QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide.

    PubMed

    Qin, Li-Tang; Chen, Yu-Han; Zhang, Xin; Mo, Ling-Yun; Zeng, Hong-Hu; Liang, Yan-Peng

    2018-05-01

    Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC 50 ) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.

  20. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2015-04-01

    Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

    PubMed

    Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

    2016-01-01

    Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

  2. Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis.

    PubMed

    Rescifina, Antonio; Floresta, Giuseppe; Marrazzo, Agostino; Parenti, Carmela; Prezzavento, Orazio; Nastasi, Giovanni; Dichiara, Maria; Amata, Emanuele

    2017-08-30

    For the first time in sigma-2 (σ 2 ) receptor field, a quantitative structure-activity relationship (QSAR) model has been built using pK i values of the whole set of known selective σ 2 receptor ligands (548 compounds), taken from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) (http://www.researchdsf.unict.it/S2RSLDB/), through the Monte Carlo technique and employing the software CORAL. The model has been developed by using a large and structurally diverse set of compounds, allowing for a prediction of different populations of chemical compounds endpoint (σ 2 receptor pK i ). The statistical quality reached, suggested that model for pK i determination is robust and possesses a satisfactory predictive potential. The statistical quality is high for both visible and invisible sets. The screening of the FDA approved drugs, external to our dataset, suggested that sixteen compounds might be repositioned as σ 2 receptor ligands (predicted pK i ≥8). A literature check showed that six of these compounds have already been tested for affinity at σ 2 receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ 2 receptor pK i >7. This suggests that this QSAR model may be used as focusing screening filter in order to prospectively find or repurpose new drugs with high affinity for the σ 2 receptor, and overall allowing for an enhanced hit rate respect to a random screening. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  4. A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

    PubMed Central

    Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

    2015-01-01

    B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained. PMID:26035757

  5. A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

    PubMed

    Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

    2015-05-29

    B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

  6. Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors.

    PubMed

    Dai, Yujie; Wang, Qiang; Zhang, Xiuli; Jia, Shiru; Zheng, Heng; Feng, Dacheng; Yu, Peng

    2010-12-01

    In order to develop more potent, selective and less toxic steroidal aromatase (AR) inhibitors, molecular docking, 2D and 3D hybrid quantitative structure-activity relationship (QSAR) study have been conducted using topological, molecular shape, spatial, structural and thermodynamic descriptors on 32 steroidal compounds. The molecular docking study shows that one or more hydrogen bonds with MET374 are one of the essential requirements for the optimum binding of ligands. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3_C, Jurs_WNSA_1, Jurs_WPSA_1 and decreasing CDOCKER interaction energy (ECD), IAC_Total and Shadow_XZfrac. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

  7. Quantitative studies on structure-ORAC relationships of anthocyanins from eggplant and radish using 3D-QSAR.

    PubMed

    Jing, Pu; Zhao, Shujuan; Ruan, Siyu; Sui, Zhongquan; Chen, Lihong; Jiang, Linlei; Qian, Bingjun

    2014-02-15

    The 3-dimensional quantitative structure activity relationship (3D-QSAR) models were established from 21 anthocyanins based on their oxygen radical absorbing capacity (ORAC) and were applied to predict anthocyanins in eggplant and radish for their ORAC values. The cross-validated q(2)=0.857/0.729, non-cross-validated r(2) = 0.958/0.856, standard error of estimate = 0.153/0.134, and F = 73.267/19.247 were for the best QSAR (CoMFA/CoMSIA) models, where the correlation coefficient r(2)pred = 0.998/0.997 (>0.6) indicated a high predictive ability for each. Additionally, the contour map results suggested that structural characteristics of anthocyanins favourable for the high ORAC. Four anthocyanins from eggplant and radish have been screened based on the QSAR models. Pelargonidin-3-[(6''-p-coumaroyl)-glucosyl(2 → 1)glucoside]-5-(6''-malonyl)-glucoside, delphinidin-3-rutinoside-5-glucoside, and delphinidin-3-[(4''-p-coumaroyl)-rhamnosyl(1 → 6)glucoside]-5-glucoside potential with high ORAC based the QSAR models were isolated and also confirmed for their relative high antioxidant ability, which might attribute to the bulky and/or electron-donating substituent at the 3-position in the C ring or/and hydrogen bond donor group/electron donating group on the R1 position in the B ring. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. 2D-QSAR and 3D-QSAR/CoMSIA Studies on a Series of (R)-2-((2-(1H-Indol-2-yl)ethyl)amino)-1-Phenylethan-1-ol with Human β₃-Adrenergic Activity.

    PubMed

    Apablaza, Gastón; Montoya, Luisa; Morales-Verdejo, Cesar; Mellado, Marco; Cuellar, Mauricio; Lagos, Carlos F; Soto-Delgado, Jorge; Chung, Hery; Pessoa-Mahana, Carlos David; Mella, Jaime

    2017-03-05

    The β₃ adrenergic receptor is raising as an important drug target for the treatment of pathologies such as diabetes, obesity, depression, and cardiac diseases among others. Several attempts to obtain selective and high affinity ligands have been made. Currently, Mirabegron is the only available drug on the market that targets this receptor approved for the treatment of overactive bladder. However, the FDA (Food and Drug Administration) in USA and the MHRA (Medicines and Healthcare products Regulatory Agency) in UK have made reports of potentially life-threatening side effects associated with the administration of Mirabegron, casting doubts on the continuity of this compound. Therefore, it is of utmost importance to gather information for the rational design and synthesis of new β₃ adrenergic ligands. Herein, we present the first combined 2D-QSAR (two-dimensional Quantitative Structure-Activity Relationship) and 3D-QSAR/CoMSIA (three-dimensional Quantitative Structure-Activity Relationship/Comparative Molecular Similarity Index Analysis) study on a series of potent β₃ adrenergic agonists of indole-alkylamine structure. We found a series of changes that can be made in the steric, hydrogen-bond donor and acceptor, lipophilicity and molar refractivity properties of the compounds to generate new promising molecules. Finally, based on our analysis, a summary and a regiospecific description of the requirements for improving β₃ adrenergic activity is given.

  9. Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models.

    PubMed

    Chen, Ying; Cai, Xiaoyu; Jiang, Long; Li, Yu

    2016-02-01

    Based on the experimental data of octanol-air partition coefficients (KOA) for 19 polychlorinated biphenyl (PCB) congeners, two types of QSAR methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), are used to establish 3D-QSAR models using the structural parameters as independent variables and using logKOA values as the dependent variable with the Sybyl software to predict the KOA values of the remaining 190 PCB congeners. The whole data set (19 compounds) was divided into a training set (15 compounds) for model generation and a test set (4 compounds) for model validation. As a result, the cross-validation correlation coefficient (q(2)) obtained by the CoMFA and CoMSIA models (shuffled 12 times) was in the range of 0.825-0.969 (>0.5), the correlation coefficient (r(2)) obtained was in the range of 0.957-1.000 (>0.9), and the SEP (standard error of prediction) of test set was within the range of 0.070-0.617, indicating that the models were robust and predictive. Randomly selected from a set of models, CoMFA analysis revealed that the corresponding percentages of the variance explained by steric and electrostatic fields were 23.9% and 76.1%, respectively, while CoMSIA analysis by steric, electrostatic and hydrophobic fields were 0.6%, 92.6%, and 6.8%, respectively. The electrostatic field was determined as a primary factor governing the logKOA. The correlation analysis of the relationship between the number of Cl atoms and the average logKOA values of PCBs indicated that logKOA values gradually increased as the number of Cl atoms increased. Simultaneously, related studies on PCB detection in the Arctic and Antarctic areas revealed that higher logKOA values indicate a stronger PCB migration ability. From CoMFA and CoMSIA contour maps, logKOA decreased when substituents possessed electropositive groups at the 2-, 3-, 3'-, 5- and 6- positions, which could reduce the PCB migration ability. These results are

  10. Identification of phototransformation products of thalidomide and mixture toxicity assessment: an experimental and quantitative structural activity relationships (QSAR) approach.

    PubMed

    Mahmoud, Waleed M M; Toolaram, Anju P; Menz, Jakob; Leder, Christoph; Schneider, Mandy; Kümmerer, Klaus

    2014-02-01

    The fate of thalidomide (TD) was investigated after irradiation with a medium-pressure Hg-lamp. The primary elimination of TD was monitored and structures of phototransformation products (PTPs) were assessed by LC-UV-FL-MS/MS. Environmentally relevant properties of TD and its PTPs as well as hydrolysis products (HTPs) were predicted using in silico QSAR models. Mutagenicity of TD and its PTPs was investigated in the Ames microplate format (MPF) aqua assay (Xenometrix, AG). Furthermore, a modified luminescent bacteria test (kinetic luminescent bacteria test (kinetic LBT)), using the luminescent bacteria species Vibrio fischeri, was applied for the initial screening of environmental toxicity. Additionally, toxicity of phthalimide, one of the identified PTPs, was investigated separately in the kinetic LBT. The UV irradiation eliminated TD itself without complete mineralization and led to the formation of several PTPs. TD and its PTPs did not exhibit mutagenic response in the Salmonella typhimurium strains TA 98, and TA 100 with and without metabolic activation. In contrast, QSAR analysis of PTPs and HTPs provided evidence for mutagenicity, genotoxicity and carcinogenicity using additional endpoints in silico software. QSAR analysis of different ecotoxicological endpoints, such as acute toxicity towards V. fischeri, provided positive alerts for several identified PTPs and HTPs. This was partially confirmed by the results of the kinetic LBT, in which a steady increase of acute and chronic toxicity during the UV-treatment procedure was observed for the photolytic mixtures at the highest tested concentration. Moreover, the number of PTPs within the reaction mixture that might be responsible for the toxification of TD during UV-treatment was successfully narrowed down by correlating the formation kinetics of PTPs with QSAR predictions and experimental toxicity data. Beyond that, further analysis of the commercially available PTP phthalimide indicated that transformation of

  11. Docking Analysis and Multidimensional Hybrid QSAR Model of 1,4-Benzodiazepine-2,5-Diones as HDM2 Antagonists

    PubMed Central

    Dai, Yujie; Chen, Nan; Wang, Qiang; Zheng, Heng; Zhang, Xiuli; Jia, Shiru; Dong, Lilong; Feng, Dacheng

    2012-01-01

    The inhibitors of p53-HDM2 interaction are attractive molecules for the treatment of wild-type p53 tumors. In order to search more potent HDM2 inhibitors, docking operation with CDOCKER protocol in Discovery Studio 2.1 (DS2.1) and multidimensional hybrid quantitative structure-activity relationship (QSAR) studies through the physiochemical properties obtained from DS2.1 and E-Dragon 1.0 as descriptors, have been performed on 59 1,4-benzodiazepine- 2,5-diones which have p53-HDM2 interaction inhibitory activities. The docking results indicate that π-π interaction between the imidazole group in HIS96 and the aryl ring at 4-N of 1,4-benzodiazepine-2,5-dione may be one of the key factors for the combination of ligands with HDM2. Two QSAR models were obtained using genetic function approximation (GFA) and genetic partial least squares (G/PLS) based on the descriptors obtained from DS2.1 and E-dragon 1.0, respectively. The best model can explain 85.5% of the variance (R 2adj ) while it could predict 81.7% of the variance (R 2 cv ). With this model, the bioactivities of some new compounds were predicted. PMID:24250508

  12. QSAR analysis for nano-sized layered manganese-calcium oxide in water oxidation: An application of chemometric methods in artificial photosynthesis.

    PubMed

    Shahbazy, Mohammad; Kompany-Zareh, Mohsen; Najafpour, Mohammad Mahdi

    2015-11-01

    Water oxidation is among the most important reactions in artificial photosynthesis, and nano-sized layered manganese-calcium oxides are efficient catalysts toward this reaction. Herein, a quantitative structure-activity relationship (QSAR) model was constructed to predict the catalytic activities of twenty manganese-calcium oxides toward water oxidation using multiple linear regression (MLR) and genetic algorithm (GA) for multivariate calibration and feature selection, respectively. Although there are eight controlled parameters during synthesizing of the desired catalysts including ripening time, temperature, manganese content, calcium content, potassium content, the ratio of calcium:manganese, the average manganese oxidation state and the surface of catalyst, by using GA only three of them (potassium content, the ratio of calcium:manganese and the average manganese oxidation state) were selected as the most effective parameters on catalytic activities of these compounds. The model's accuracy criteria such as R(2)test and Q(2)test in order to predict catalytic rate for external test set experiments; were equal to 0.941 and 0.906, respectively. Therefore, model reveals acceptable capability to anticipate the catalytic activity. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. QSAR study of curcumine derivatives as HIV-1 integrase inhibitors.

    PubMed

    Gupta, Pawan; Sharma, Anju; Garg, Prabha; Roy, Nilanjan

    2013-03-01

    A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r(2)) 0.891 and cross validated r(2) (r(2) cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r(2) pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.

  14. Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water.

    PubMed

    Liu, Huihui; Wei, Mengbi; Yang, Xianhai; Yin, Cen; He, Xiao

    2017-01-01

    Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (K pew ). For chemicals with the octanol-water partition coefficient (log K ow ) <8, a TLSER model with V x (McGowan volume) and qA - (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R 2 ) and cross-validated coefficient (Q 2 ). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log K OW >8, a TLSER model with V x and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Aconitum and Delphinium sp. Alkaloids as Antagonist Modulators of Voltage-Gated Na+ Channels. AM1/DFT Electronic Structure Investigations and QSAR Studies

    PubMed Central

    Turabekova, Malakhat A.; Rasulev, Bakhtiyor F.; Levkovich, Mikhail G.; Abdullaev, Nasrulla D.; Leszczynski, Jerzy

    2015-01-01

    Early pharmacological studies of Aconitum and Delphinium sp. alkaloids suggested that these neurotoxins act at site 2 of voltage-gated Na+ channel and allosterically modulate its function. Understanding structural requirements for these compounds to exhibit binding activity at voltage-gated Na+ channel has been important in various fields. This paper reports quantum-chemical studies and quantitative structure-activity relationships (QSARs) based on a total of 65 natural alkaloids from two plant species, which includes both blockers and openers of sodium ion channel. A series of 18 antagonist alkaloids (9 blockers and 9 openers) have been studied using AM1 and DFT computational methods in order to reveal their structure-activity (structure-toxicity) relationship at electronic level. An examination of frontier orbitals obtained for ground and protonated forms of the compounds revealed that HOMOs and LUMOs were mainly represented by nitrogen atom and benzyl/benzoylester orbitals with –OH and –OCOCH3 contributions. The results obtained from this research have confirmed the experimental findings suggesting that neurotoxins acting at type 2 receptor site of voltage-dependent sodium channel are activators and blockers with common structural features and differ only in efficacy. The energetic tendency of HOMO-LUMO energy gap can probably distinguish activators and blockers that have been observed. Genetic Algorithm with Multiple Linear Regression Analysis (GA-MLRA) technique was also applied for the generation of two-descriptor QSAR models for the set of 65 blockers. Additionally to the computational studies, the HOMO-LUMO gap descriptor in each obtained QSAR model has confirmed the crucial role of charge transfer in receptor-ligand interactions. A number of other descriptors such as logP, IBEG, nNH2, nHDon, nCO have been selected as complementary ones to LUMO and their role in activity alteration has also been discussed. PMID:18201930

  16. 3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase Inhibitors: Analogues of the Active Metabolite of Leflunomide

    PubMed Central

    Li, Shun-Lai; He, Mao-Yu; Du, Hong-Guang

    2011-01-01

    The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated rCV2 (0.664) and non cross-validated r2 (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme. PMID:21686163

  17. Insight into the interaction mechanism of human SGLT2 with its inhibitors: 3D-QSAR studies, homology modeling, and molecular docking and molecular dynamics simulations.

    PubMed

    Dong, Lili; Feng, Ruirui; Bi, Jiawei; Shen, Shengqiang; Lu, Huizhe; Zhang, Jianjun

    2018-03-06

    Human sodium-dependent glucose co-transporter 2 (hSGLT2) is a crucial therapeutic target in the treatment of type 2 diabetes. In this study, both comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to generate three-dimensional quantitative structure-activity relationship (3D-QSAR) models. In the most accurate CoMFA-based and CoMSIA-based QSAR models, the cross-validated coefficients (r 2 cv ) were 0.646 and 0.577, respectively, while the non-cross-validated coefficients (r 2 ) were 0.997 and 0.991, respectively, indicating that both models were reliable. In addition, we constructed a homology model of hSGLT2 in the absence of a crystal structure. Molecular docking was performed to explore the bonding mode of inhibitors to the active site of hSGLT2. Molecular dynamics (MD) simulations and binding free energy calculations using MM-PBSA and MM-GBSA were carried out to further elucidate the interaction mechanism. With regards to binding affinity, we found that hydrogen-bond interactions of Asn51 and Glu75, located in the active site of hSGLT2, with compound 40 were critical. Hydrophobic and electrostatic interactions were shown to enhance activity, in agreement with the results obtained from docking and 3D-QSAR analysis. Our study results shed light on the interaction mode between inhibitors and hSGLT2 and may aid in the development of C-aryl glucoside SGLT2 inhibitors.

  18. QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds.

    PubMed

    Pérez-Garrido, Alfonso; Helguera, Aliuska Morales; Rodríguez, Francisco Girón; Cordeiro, M Natália D S

    2010-05-01

    The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals. Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software. For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers. These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk. 2010 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

  19. Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies.

    PubMed

    Pingaew, Ratchanok; Prachayasittikul, Veda; Worachartcheewan, Apilak; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

    2015-10-20

    A novel series of 1,4-naphthoquinones (33-44) tethered by open and closed chain sulfonamide moieties were designed, synthesized and evaluated for their cytotoxic and antimalarial activities. All quinone-sulfonamide derivatives displayed a broad spectrum of cytotoxic activities against all of the tested cancer cell lines including HuCCA-1, HepG2, A549 and MOLT-3. Most quinones (33-36 and 38-43) exerted higher anticancer activity against HepG2 cell than that of the etoposide. The open chain analogs 36 and 42 were shown to be the most potent compounds. Notably, the restricted sulfonamide analog 38 with 6,7-dimethoxy groups exhibited the most potent antimalarial activity (IC₅₀ = 2.8 μM). Quantitative structure-activity relationships (QSAR) study was performed to reveal important chemical features governing the biological activities. Five constructed QSAR models provided acceptable predictive performance (Rcv 0.5647-0.9317 and RMSEcv 0.1231-0.2825). Four additional sets of structurally modified compounds were generated in silico (34a-34d, 36a-36k, 40a-40d and 42a-42k) in which their activities were predicted using the constructed QSAR models. A comprehensive discussion of the structure-activity relationships was made and a set of promising compounds (i.e., 33, 36, 38, 42, 36d, 36f, 42e, 42g and 42f) was suggested for further development as anticancer and antimalarial agents. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  20. Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

    PubMed

    Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

    2017-02-01

    High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

  1. A combined QSAR and partial order ranking approach to risk assessment.

    PubMed

    Carlsen, L

    2006-04-01

    QSAR generated data appear as an attractive alternative to experimental data as foreseen in the proposed new chemicals legislation REACH. A preliminary risk assessment for the aquatic environment can be based on few factors, i.e. the octanol-water partition coefficient (Kow), the vapour pressure (VP) and the potential biodegradability of the compound in combination with the predicted no-effect concentration (PNEC) and the actual tonnage in which the substance is produced. Application of partial order ranking, allowing simultaneous inclusion of several parameters leads to a mutual prioritisation of the investigated substances, the prioritisation possibly being further analysed through the concept of linear extensions and average ranks. The ranking uses endpoint values (log Kow and log VP) derived from strictly linear 'noise-deficient' QSAR models as input parameters. Biodegradation estimates were adopted from the BioWin module of the EPI Suite. The population growth impairment of Tetrahymena pyriformis was used as a surrogate for fish lethality.

  2. Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.

    PubMed

    Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

    2013-04-01

    Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

  3. Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

    EPA Science Inventory

    This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

  4. Toward the identification of a reliable 3D-QSAR model for the protein tyrosine phosphatase 1B inhibitors

    NASA Astrophysics Data System (ADS)

    Wang, Fangfang; Zhou, Bo

    2018-04-01

    Protein tyrosine phosphatase 1B (PTP1B) is an intracellular non-receptor phosphatase that is implicated in signal transduction of insulin and leptin pathways, thus PTP1B is considered as potential target for treating type II diabetes and obesity. The present article is an attempt to formulate the three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling of a series of compounds possessing PTP1B inhibitory activities using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The optimum template ligand-based models are statistically significant with great CoMFA (R2cv = 0.600, R2pred = 0.6760) and CoMSIA (R2cv = 0.624, R2pred = 0.8068) values. Molecular docking was employed to elucidate the inhibitory mechanisms of this series of compounds against PTP1B. In addition, the CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of PTP1B active site. The knowledge of structure-activity relationship and ligand-receptor interactions from 3D-QSAR model and molecular docking will be useful for better understanding the mechanism of ligand-receptor interaction and facilitating development of novel compounds as potent PTP1B inhibitors.

  5. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    PubMed Central

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2015-01-01

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

  6. Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

    PubMed

    Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

    2014-10-01

    Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

  7. A 3D QSAR pharmacophore model and quantum chemical structure--activity analysis of chloroquine(CQ)-resistance reversal.

    PubMed

    Bhattacharjee, Apurba K; Kyle, Dennis E; Vennerstrom, Jonathan L; Milhous, Wilbur K

    2002-01-01

    Using CATALYST, a three-dimensional QSAR pharmacophore model for chloroquine(CQ)-resistance reversal was developed from a training set of 17 compounds. These included imipramine (1), desipramine (2), and 15 of their analogues (3-17), some of which fully reversed CQ-resistance, while others were without effect. The generated pharmacophore model indicates that two aromatic hydrophobic interaction sites on the tricyclic ring and a hydrogen bond acceptor (lipid) site at the side chain, preferably on a nitrogen atom, are necessary for potent activity. Stereoelectronic properties calculated by using AM1 semiempirical calculations were consistent with the model, particularly the electrostatic potential profiles characterized by a localized negative potential region by the side chain nitrogen atom and a large region covering the aromatic ring. The calculated data further revealed that aminoalkyl substitution at the N5-position of the heterocycle and a secondary or tertiary aliphatic aminoalkyl nitrogen atom with a two or three carbon bridge to the heteroaromatic nitrogen (N5) are required for potent "resistance reversal activity". Lowest energy conformers for 1-17 were determined and optimized to afford stereoelectronic properties such as molecular orbital energies, electrostatic potentials, atomic charges, proton affinities, octanol-water partition coefficients (log P), and structural parameters. For 1-17, fairly good correlation exists between resistance reversal activity and intrinsic basicity of the nitrogen atom at the tricyclic ring system, frontier orbital energies, and lipophilicity. Significantly, nine out of 11 of a group of structurally diverse CQ-resistance reversal agents mapped very well on the 3D QSAR pharmacophore model.

  8. Effect of dissolved organic matter on pre-equilibrium passive sampling: A predictive QSAR modeling study.

    PubMed

    Lin, Wei; Jiang, Ruifen; Shen, Yong; Xiong, Yaxin; Hu, Sizi; Xu, Jianqiao; Ouyang, Gangfeng

    2018-04-13

    Pre-equilibrium passive sampling is a simple and promising technique for studying sampling kinetics, which is crucial to determine the distribution, transfer and fate of hydrophobic organic compounds (HOCs) in environmental water and organisms. Environmental water samples contain complex matrices that complicate the traditional calibration process for obtaining the accurate rate constants. This study proposed a QSAR model to predict the sampling rate constants of HOCs (polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and pesticides) in aqueous systems containing complex matrices. A homemade flow-through system was established to simulate an actual aqueous environment containing dissolved organic matter (DOM) i.e. humic acid (HA) and (2-Hydroxypropyl)-β-cyclodextrin (β-HPCD)), and to obtain the experimental rate constants. Then, a quantitative structure-activity relationship (QSAR) model using Genetic Algorithm-Multiple Linear Regression (GA-MLR) was found to correlate the experimental rate constants to the system state including physicochemical parameters of the HOCs and DOM which were calculated and selected as descriptors by Density Functional Theory (DFT) and Chem 3D. The experimental results showed that the rate constants significantly increased as the concentration of DOM increased, and the enhancement factors of 70-fold and 34-fold were observed for the HOCs in HA and β-HPCD, respectively. The established QSAR model was validated as credible (R Adj. 2 =0.862) and predictable (Q 2 =0.835) in estimating the rate constants of HOCs for complex aqueous sampling, and a probable mechanism was developed by comparison to the reported theoretical study. The present study established a QSAR model of passive sampling rate constants and calibrated the effect of DOM on the sampling kinetics. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs.

    PubMed

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Igglessi-Markopoulou, Olga; Kollias, George

    2010-05-01

    A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R(LOO2)) = 0.67) and validation through an external test set (R(pred2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.

  10. QSAR modeling based on structure-information for properties of interest in human health.

    PubMed

    Hall, L H; Hall, L M

    2005-01-01

    The development of QSAR models based on topological structure description is presented for problems in human health. These models are based on the structure-information approach to quantitative biological modeling and prediction, in contrast to the mechanism-based approach. The structure-information approach is outlined, starting with basic structure information developed from the chemical graph (connection table). Information explicit in the connection table (element identity and skeletal connections) leads to significant (implicit) structure information that is useful for establishing sound models of a wide range of properties of interest in drug design. Valence state definition leads to relationships for valence state electronegativity and atom/group molar volume. Based on these important aspects of molecules, together with skeletal branching patterns, both the electrotopological state (E-state) and molecular connectivity (chi indices) structure descriptors are developed and described. A summary of four QSAR models indicates the wide range of applicability of these structure descriptors and the predictive quality of QSAR models based on them: aqueous solubility (5535 chemically diverse compounds, 938 in external validation), percent oral absorption (%OA, 417 therapeutic drugs, 195 drugs in external validation testing), AMES mutagenicity (2963 compounds including 290 therapeutic drugs, 400 in external validation), fish toxicity (92 substituted phenols, anilines and substituted aromatics). These models are established independent of explicit three-dimensional (3-D) structure information and are directly interpretable in terms of the implicit structure information useful to the drug design process.

  11. Linear indices of the "molecular pseudograph's atom adjacency matrix": definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors.

    PubMed

    Marrero-Ponce, Yovani

    2004-01-01

    This report describes a new set of molecular descriptors of relevance to QSAR/QSPR studies and drug design, atom linear indices fk(xi). These atomic level chemical descriptors are based on the calculation of linear maps on Rn[fk(xi): Rn--> Rn] in canonical basis. In this context, the kth power of the molecular pseudograph's atom adjacency matrix [Mk(G)] denotes the matrix of fk(xi) with respect to the canonical basis. In addition, a local-fragment (atom-type) formalism was developed. The kth atom-type linear indices are calculated by summing the kth atom linear indices of all atoms of the same atom type in the molecules. Moreover, total (whole-molecule) linear indices are also proposed. This descriptor is a linear functional (linear form) on Rn. That is, the kth total linear indices is a linear map from Rn to the scalar R[ fk(x): Rn --> R]. Thus, the kth total linear indices are calculated by summing the atom linear indices of all atoms in the molecule. The features of the kth total and local linear indices are illustrated by examples of various types of molecular structures, including chain-lengthening, branching, heteroatoms-content, and multiple bonds. Additionally, the linear independence of the local linear indices to other 0D, 1D, 2D, and 3D molecular descriptors is demonstrated by using principal component analysis for 42 very heterogeneous molecules. Much redundancy and overlapping was found among total linear indices and most of the other structural indices presently in use in the QSPR/QSAR practice. On the contrary, the information carried by atom-type linear indices was strikingly different from that codified in most of the 229 0D-3D molecular descriptors used in this study. It is concluded that the local linear indices are an independent indices containing important structural information to be used in QSPR/QSAR and drug design studies. In this sense, atom, atom-type, and total linear indices were used for the prediction of pIC50 values for the cleavage

  12. Application of 3D-QSAR, Pharmacophore, and Molecular Docking in the Molecular Design of Diarylpyrimidine Derivatives as HIV-1 Nonnucleoside Reverse Transcriptase Inhibitors.

    PubMed

    Liu, Genyan; Wang, Wenjie; Wan, Youlan; Ju, Xiulian; Gu, Shuangxi

    2018-05-11

    Diarylpyrimidines (DAPYs), acting as HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs), have been considered to be one of the most potent drug families in the fight against acquired immunodeficiency syndrome (AIDS). To better understand the structural requirements of HIV-1 NNRTIs, three-dimensional quantitative structure⁻activity relationship (3D-QSAR), pharmacophore, and molecular docking studies were performed on 52 DAPY analogues that were synthesized in our previous studies. The internal and external validation parameters indicated that the generated 3D-QSAR models, including comparative molecular field analysis (CoMFA, q 2 = 0.679, R 2 = 0.983, and r pred 2 = 0.884) and comparative molecular similarity indices analysis (CoMSIA, q 2 = 0.734, R 2 = 0.985, and r pred 2 = 0.891), exhibited good predictive abilities and significant statistical reliability. The docking results demonstrated that the phenyl ring at the C₄-position of the pyrimidine ring was better than the cycloalkanes for the activity, as the phenyl group was able to participate in π⁻π stacking interactions with the aromatic residues of the binding site, whereas the cycloalkanes were not. The pharmacophore model and 3D-QSAR contour maps provided significant insights into the key structural features of DAPYs that were responsible for the activity. On the basis of the obtained information, a series of novel DAPY analogues of HIV-1 NNRTIs with potentially higher predicted activity was designed. This work might provide useful information for guiding the rational design of potential HIV-1 NNRTI DAPYs.

  13. QSAR models for prediction of chromatographic behavior of homologous Fab variants.

    PubMed

    Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

    2017-06-01

    While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  14. Quantitative structure-activity relationships (QSAR) of some 2,2-diphenyl propionate (DPP) derivatives of muscarinic antagonists

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gordon, R.K.; Breuer, E.; Padilla, F.N.

    1987-05-01

    QSAR between biological activities and molecular-chemical properties were investigated to aid in designing more effective and potent antimuscarinic pharmacophores. A molecular modeling program was used to calculate geometrical and topological values of a series of DPP pharmacophores. The newly synthesized pharmacophores were tested for their antagonist activities by: (1) inhibition of (N-methyl-/sup 3/H)scopolamine binding assay to the muscarinic receptors of N4TG1 neuroblastoma cells; (2) blocking of acetylcholine-induced contraction of guinea pig ileum; and (3) inhibition of carbachol-induced ..cap alpha..-amylase release from rat pancreas. The differences in the log of these biological activities were directly and significantly related to the distancesmore » between the carbonyl oxygen of the DPP and the quaternary nitrogen of the modified pharmacophores. The biological activities, while depending on each particular assay, varied between three and four logs of activity. The charge remained the same in all the pharmacophores. There were no QSAR correlations between molecular volume, molecular connectivity, or principle moments and their antagonistic activities, although multivariate QSAR was not employed. Thus, based on distance geometry, potent muscarinic pharmacophores can be predicted.« less

  15. Application of quantitative structure activity relationship (QSAR) models to predict ozone toxicity in the lung.

    PubMed

    Kafoury, Ramzi M; Huang, Ming-Ju

    2005-08-01

    The sequence of events leading to ozone-induced airway inflammation is not well known. To elucidate the molecular and cellular events underlying ozone toxicity in the lung, we hypothesized that lipid ozonation products (LOPs) generated by the reaction of ozone with unsaturated fatty acids in the epithelial lining fluid and cell membranes play a key role in mediating ozone-induced airway inflammation. To test our hypothesis, we ozonized 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) and generated LOPs. Confluent human bronchial epithelial cells were exposed to the derivatives of ozonized POPC-9-oxononanoyl, 9-hydroxy-9-hydroperoxynonanoyl, and 8-(5-octyl-1,2,4-trioxolan-3-yl-)octanoyl-at a concentration of 10 muM, and the activity of phospholipases A2 (PLA2), C (PLC), and D (PLD) was measured (1, 0.5, and 1 h, respectively). Quantitative structure-activity relationship (QSAR) models were utilized to predict the biological activity of LOPs in airway epithelial cells. The QSAR results showed a strong correlation between experimental and computed activity (r = 0.97, 0.98, 0.99, for PLA2, PLC, and PLD, respectively). The results indicate that QSAR models can be utilized to predict the biological activity of the various ozone-derived LOP species in the lung. Copyright 2005 Wiley Periodicals, Inc.

  16. QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation†

    PubMed Central

    Jensen, G.E.; Niemelä, J.R.; Wedebye, E.B.; Nikolov, N.G.

    2008-01-01

    A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. PMID:19061080

  17. Automated workflows for data curation and standardization of chemical structures for QSAR modeling

    EPA Science Inventory

    Large collections of chemical structures and associated experimental data are publicly available, and can be used to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experime...

  18. 20180312 - Structure-based QSAR Models to Predict Systemic Toxicity Points of Departure (SOT)

    EPA Science Inventory

    Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models base...

  19. Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity

    PubMed Central

    Fjodorova, Natalja; Novič, Marjana

    2012-01-01

    The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals. PMID:24688639

  20. A QSAR Model for Thyroperoxidase Inhibition and Screening ...

    EPA Pesticide Factsheets

    Thyroid hormones (THs) are critical modulators of a wide range of biological processes from neurodevelopment to metabolism. Well regulated levels of THs are critical during development and even moderate changes in maternal or fetal TH levels produce irreversible neurological deficits in children. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs. Inhibition of TPO by xenobiotics leads to decreased TH synthesis and, depending on the degree of synthesis inhibition, may result in adverse developmental outcomes. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to screen the ToxCast Phase I and II chemicals. In the present study, we used the results from the AUR-TPO screening to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition in Leadscope®. The training set consisted of 898 discrete organic chemicals: 134 positive and 764 negative for TPO inhibition. A 10 times two-fold 50% cross-validation of the model was performed, yielding a balanced accuracy of 78.7% within its defined applicability domain. More recently, an additional ~800 chemicals from the US EPA Endocrine Disruption Screening Program (EDSP21) were screened using the AUR-TPO assay. This data was used for external validation of the QSAR model, demonstrating a balanced accuracy of 85.7% within its applicability domain. Overall, the cross- and external validations indicate a model with a high predictiv

  1. QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study.

    PubMed

    Ferrari, Thomas; Lombardo, Anna; Benfenati, Emilio

    2018-05-14

    Several methods exist to develop QSAR models automatically. Some are based on indices of the presence of atoms, other on the most similar compounds, other on molecular descriptors. Here we introduce QSARpy v1.0, a new QSAR modeling tool based on a different approach: the dissimilarity. This tool fragments the molecules of the training set to extract fragments that can be associated to a difference in the property/activity value, called modulators. If the target molecule share part of the structure with a molecule of the training set and differences can be explained with one or more modulators, the property/activity value of the molecule of the training set is adjusted using the value associated to the modulator(s). This tool is tested here on the n-octanol/water partition coefficient (Kow, usually expressed in logarithmic units as log Kow). It is a key parameter in risk assessment since it is a measure of hydrophobicity. Its wide spread use makes these estimation methods very useful to reduce testing costs. Using QSARpy v1.0, we obtained a new model to predict log Kow with accurate performance (RMSE 0.43 and R 2 0.94 for the external test set), comparing favorably with other programs. QSARpy is freely available on request. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient.

    PubMed

    Chirico, Nicola; Gramatica, Paola

    2011-09-26

    The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schüürmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive.

  3. Development of a QSAR model for predicting aqueous reaction rate constants of organic chemicals with hydroxyl radicals.

    PubMed

    Luo, Xiang; Yang, Xianhai; Qiao, Xianliang; Wang, Ya; Chen, Jingwen; Wei, Xiaoxuan; Peijnenburg, Willie J G M

    2017-03-22

    Reaction with hydroxyl radicals (˙OH) is an important removal pathway for organic pollutants in the aquatic environment. The aqueous reaction rate constant (k OH ) is therefore an important parameter for fate assessment of aquatic pollutants. Since experimental determination fails to meet the requirement of being able to efficiently handle numerous organic chemicals at limited cost and within a relatively short period of time, in silico methods such as quantitative structure-activity relationship (QSAR) models are needed to predict k OH . In this study, a QSAR model with a larger and wider applicability domain as compared with existing models was developed. Following the guidelines for the development and validation of QSAR models proposed by the Organization for Economic Co-operation and Development (OECD), the model shows satisfactory performance. The applicability domain of the model has been extended and contained chemicals that have rarely been covered in most previous studies. The chemicals covered in the current model contain functional groups including [double bond splayed left]C[double bond, length as m-dash]C[double bond splayed right], -C[triple bond, length as m-dash]C-, -C 6 H 5 , -OH, -CHO, -O-, [double bond splayed left]C[double bond, length as m-dash]O, -C[double bond, length as m-dash]O(O)-, -COOH, -C[triple bond, length as m-dash]N, [double bond splayed left]N-, -NH 2 , -NH-C(O)-, -NO 2 , -N[double bond, length as m-dash]C-N[double bond splayed right], [double bond splayed left]N-N[double bond splayed right], -N[double bond, length as m-dash]N-, -S-, -S-S-, -SH, -SO 3 , -SO 4 , -PO 4 , and -X (F, Cl, Br, and I).

  4. Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

    PubMed Central

    Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

    2011-01-01

    Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

  5. Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

    PubMed Central

    Sinha, Siddharth; Goyal, Sukriti; Somvanshi, Pallavi; Grover, Abhinav

    2017-01-01

    Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r2) value of 0.6774, cross-validated correlation coefficient (q2) of 0.6157 and co-relation coefficient for external test set (pred_r2) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free

  6. Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach.

    PubMed

    Sinha, Siddharth; Goyal, Sukriti; Somvanshi, Pallavi; Grover, Abhinav

    2016-01-01

    Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient ( r 2 ) value of 0.6774, cross-validated correlation coefficient ( q 2 ) of 0.6157 and co-relation coefficient for external test set ( pred _ r 2 ) of 0.7570. A high F -test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of -10.097 and -9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with

  7. Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testing.

    PubMed

    Sullivan, K M; Manuppello, J R; Willett, C E

    2014-01-01

    The development of more efficient, ethical, and effective means of assessing the effects of chemicals on human health and the environment was a lifetime goal of Gilman Veith. His work has provided the foundation for the use of chemical structure for informing toxicological assessment by regulatory agencies the world over. Veith's scientific work influenced the early development of the SAR models in use today at the US Environmental Protection Agency. He was the driving force behind the Organisation for Economic Co-operation and Development QSAR Toolbox. Veith was one of a few early pioneers whose vision led to the linkage of chemical structure and biological activity as a means of predicting adverse apical outcomes (known as a mode of action, or an adverse outcome pathway approach), and he understood at an early stage the power that could be harnessed when combining computational and mechanistic biological approaches as a means of avoiding animal testing. Through the International QSAR Foundation he organized like-minded experts to develop non-animal methods and frameworks for the assessment of chemical hazard and risk for the benefit of public and environmental health. Avoiding animal testing was Gil's passion, and his work helped to initiate the paradigm shift in toxicology that is now rendering this feasible.

  8. Studies of New Fused Benzazepine as Selective Dopamine D3 Receptor Antagonists Using 3D-QSAR, Molecular Docking and Molecular Dynamics

    PubMed Central

    Liu, Jing; Li, Yan; Zhang, Shuwei; Xiao, Zhengtao; Ai, Chunzhi

    2011-01-01

    In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q2 = 0.603, R2ncv = 0.829, R2pre = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q2 = 0.506, R2ncv =0.838, R2pre = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R3 substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R1 substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists. PMID:21541053

  9. Studies of new fused benzazepine as selective dopamine D3 receptor antagonists using 3D-QSAR, molecular docking and molecular dynamics.

    PubMed

    Liu, Jing; Li, Yan; Zhang, Shuwei; Xiao, Zhengtao; Ai, Chunzhi

    2011-02-18

    In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q(2) = 0.603, R(2) (ncv) = 0.829, R(2) (pre) = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q(2) = 0.506, R(2) (ncv) =0.838, R(2) (pre) = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R(3) substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R(1) substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists.

  10. First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

    NASA Astrophysics Data System (ADS)

    Pandit, Amit; Sengupta, Sagnik; Krishnan, Mena Asha; Reddy, Ramesh B.; Sharma, Rajesh; Venkatesh, Chelvam

    2018-05-01

    Prostate Specific Membrane Antigen (PSMA) or Glutamate carboxypeptidase II (GCPII) has been identified as an important target in diagnosis and therapy of prostate cancer. Among several types of inhibitors, urea based inhibitors are the most common and widely employed in preclinical and clinical studies. Computational studies have been carried out to uncover active sites and interaction of PSMA inhibitors with the protein by modifying the core structure of the ligand. Analysis of the literature, however, show lack of 3-D quantitative structure activity relationship (QSAR) and molecular dynamics based molecular docking study to identify structural modifications responsible for better GCPII inhibitory activity. The present study aims to fulfil this gap by analysing well known PSMA inhibitors reported in the literature with known experimental PSMA inhibition constants. Also in order to validate the in silico study, a new GCPII inhibitor 7 was designed, synthesized and experimental PSMA enzyme inhibition was evaluated by using freshly isolated PSMA protein from human cancer cell line derived from lymph node, LNCaP. 3D-QSAR CoMFA models on 58 urea based GCPII inhibitors were generated, and the best correlation was obtained in Gast-Huck charge assigning method with q2, r2 and predictive r2 values as 0.592, 0.995 and 0.842 respectively. Moreover, steric, electrostatic, and hydrogen bond donor field contribution analysis provided best statistical values from CoMSIA model (q2, r2 and predictive r2 as 0.527, 0.981 and 0.713 respectively). Contour maps study revealed that electrostatic field contribution is the major factor for discovering better binding affinity ligands. Further molecular dynamic assisted molecular docking was also performed on GCPII receptor (PDB ID 4NGM) and most active GCPII inhibitor, DCIBzL. 4NGM co-crystallised ligand, JB7 was used to validate the docking procedure and the amino acid interactions present in JB7 are compared with DCIBzL. The results

  11. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

    PubMed Central

    Alves, Vinicius M.; Capuzzi, Stephen J.; Muratov, Eugene; Braga, Rodolpho C.; Thornton, Thomas; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2016-01-01

    Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential. PMID:28630595

  12. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

    PubMed

    Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

    2018-02-12

    We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

  13. USE OF INTERSPECIES CORRELATION ESTIMATIONS TO PREDICT HC5'S BASED ON QSAR

    EPA Science Inventory

    Dyer, S.D., S. Belanger, J. Chaney, D. Versteeg and F. Mayer. In press. Use of Interspecies Correlation Estimations to predict HC5's Based on QSARs (Abstract). To be presented at the SETAC Europe 14th Annual Meeting: Environmental Science Solution: A Pan-European Perspective, 18-...

  14. QSAR Modeling and Prediction of Drug-Drug Interactions.

    PubMed

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  15. Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling.

    PubMed Central

    Yang, R S; Thomas, R S; Gustafson, D L; Campain, J; Benjamin, S A; Verhaar, H J; Mumtaz, M M

    1998-01-01

    Systematic toxicity testing, using conventional toxicology methodologies, of single chemicals and chemical mixtures is highly impractical because of the immense numbers of chemicals and chemical mixtures involved and the limited scientific resources. Therefore, the development of unconventional, efficient, and predictive toxicology methods is imperative. Using carcinogenicity as an end point, we present approaches for developing predictive tools for toxicologic evaluation of chemicals and chemical mixtures relevant to environmental contamination. Central to the approaches presented is the integration of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and quantitative structure--activity relationship (QSAR) modeling with focused mechanistically based experimental toxicology. In this development, molecular and cellular biomarkers critical to the carcinogenesis process are evaluated quantitatively between different chemicals and/or chemical mixtures. Examples presented include the integration of PBPK/PD and QSAR modeling with a time-course medium-term liver foci assay, molecular biology and cell proliferation studies. Fourier transform infrared spectroscopic analyses of DNA changes, and cancer modeling to assess and attempt to predict the carcinogenicity of the series of 12 chlorobenzene isomers. Also presented is an ongoing effort to develop and apply a similar approach to chemical mixtures using in vitro cell culture (Syrian hamster embryo cell transformation assay and human keratinocytes) methodologies and in vivo studies. The promise and pitfalls of these developments are elaborated. When successfully applied, these approaches may greatly reduce animal usage, personnel, resources, and time required to evaluate the carcinogenicity of chemicals and chemical mixtures. Images Figure 6 PMID:9860897

  16. Understanding Substrate Selectivity of Human UDP-glucuronosyltransferases through QSAR modeling and analysis of homologous enzymes

    PubMed Central

    Dong, Dong; Ako, Roland; Hu, Ming; Wu, Baojian

    2015-01-01

    The UDP-glucuronosyltransferase (UGT) enzyme catalyzes the glucuronidation reaction which is a major metabolic and detoxification pathway in humans. Understanding the mechanisms for substrate recognition by UGT assumes great importance in an attempt to predict its contribution to xenobiotic/drug disposition in vivo. Spurred on by this interest, 2D/3D-quantitative structure activity relationships (QSAR) and pharmacophore models have been established in the absence of a complete mammalian UGT crystal structure. This review discusses the recent progress in modeling human UGT substrates including those with multiple sites of glucuronidation. A better understanding of UGT active site contributing to substrate selectivity (and regioselectivity) from the homologous enzymes (i.e., plant and bacterial UGTs, all belong to family 1 of glycosyltransferase (GT1)) is also highlighted, as these enzymes share a common catalytic mechanism and/or overlapping substrate selectivity. PMID:22385482

  17. Novel pyrrolopyridinone derivatives as anticancer inhibitors towards Cdc7: QSAR studies based on dockings by solvation score approach.

    PubMed

    Wu, Xiangxiang; Zeng, Huahui; Zhu, Xin; Ma, Qiujuan; Hou, Yimin; Wu, Xuefen

    2013-11-20

    A series of pyrrolopyridinone derivatives as specific inhibitors towards the cell division cycle 7 (Cdc7) was taken into account, and the efficacy of these compounds was analyzed by QSAR and docking approaches to gain deeper insights into the interaction mechanism and ligands selectivity for Cdc7. By regression analysis the prediction models based on Grid score and Zou-GB/SA score were found, respectively with good quality of fits (r(2)=0.748, 0.951; r(cv)(2)=0.712, 0.839). The accuracy of the models was validated by test set and the deviation of the predicted values in validation set using Zou-GB/SA score was smaller than that using Grid score, suggesting that the model based on Zou-GB/SA score provides a more effective method for predicting potencies of Cdc7 inhibitors. Copyright © 2013 Elsevier B.V. All rights reserved.

  18. Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

    NASA Astrophysics Data System (ADS)

    Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

    2008-06-01

    In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

  19. 3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY

    EPA Science Inventory

    The COmmon REactivity Pattern (COREPA) approach is a three-dimensional structure activity (3-D QSAR) technique that permits identification and quantification of specific global and local steroelectronic characteristics associated with a chemical's biological activity. It goes bey...

  20. Some Phthalocyanine and Naphthalocyanine Derivatives as Corrosion Inhibitors for Aluminium in Acidic Medium: Experimental, Quantum Chemical Calculations, QSAR Studies and Synergistic Effect of Iodide Ions.

    PubMed

    Dibetsoe, Masego; Olasunkanmi, Lukman O; Fayemi, Omolola E; Yesudass, Sasikumar; Ramaganthan, Baskar; Bahadur, Indra; Adekunle, Abolanle S; Kabanda, Mwadham M; Ebenso, Eno E

    2015-08-28

    The effects of seven macrocyclic compounds comprising four phthalocyanines (Pcs) namely 1,4,8,11,15,18,22,25-octabutoxy-29H,31H-phthalocyanine (Pc1), 2,3,9,10,16,17,23,24-octakis(octyloxy)-29H,31H-phthalocyanine (Pc2), 2,9,16,23-tetra-tert-butyl-29H,31H-phthalocyanine (Pc3) and 29H,31H-phthalocyanine (Pc4), and three naphthalocyanines namely 5,9,14,18,23,27,32,36-octabutoxy-2,3-naphthalocyanine (nPc1), 2,11,20,29-tetra-tert-butyl-2,3-naphthalocyanine (nPc2) and 2,3-naphthalocyanine (nP3) were investigated on the corrosion of aluminium (Al) in 1 M HCl using a gravimetric method, potentiodynamic polarization technique, quantum chemical calculations and quantitative structure activity relationship (QSAR). Synergistic effects of KI on the corrosion inhibition properties of the compounds were also investigated. All the studied compounds showed appreciable inhibition efficiencies, which decrease with increasing temperature from 30 °C to 70 °C. At each concentration of the inhibitor, addition of 0.1% KI increased the inhibition efficiency compared to the absence of KI indicating the occurrence of synergistic interactions between the studied molecules and I(-) ions. From the potentiodynamic polarization studies, the studied Pcs and nPcs are mixed type corrosion inhibitors both without and with addition of KI. The adsorption of the studied molecules on Al surface obeys the Langmuir adsorption isotherm, while the thermodynamic and kinetic parameters revealed that the adsorption of the studied compounds on Al surface is spontaneous and involves competitive physisorption and chemisorption mechanisms. The experimental results revealed the aggregated interactions between the inhibitor molecules and the results further indicated that the peripheral groups on the compounds affect these interactions. The calculated quantum chemical parameters and the QSAR results revealed the possibility of strong interactions between the studied inhibitors and metal surface. QSAR analysis on the

  1. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

    EPA Science Inventory

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

  2. 3D-QSAR study and design of 4-hydroxyamino α-pyranone carboxamide analogues as potential anti-HCV agents

    NASA Astrophysics Data System (ADS)

    Li, Wenlian; Xiao, Faqi; Zhou, Mingming; Jiang, Xuejin; Liu, Jun; Si, Hongzong; Xie, Meng; Ma, Xiuting; Duan, Yunbo; Zhai, Honglin

    2016-09-01

    The three dimensional-quantitative structure activity relationship (3D-QSAR) study was performed on a series of 4-hydroxyamino α-pyranone carboxamide analogues using comparative molecular similarity indices analysis (COMSIA). The purpose of the present study was to develop a satisfactory model providing a reliable prediction based on 4-hydroxyamino α-pyranone carboxamide analogues as anti-HCV (hepatitis C virus) inhibitors. The statistical results and the results of validation of this optimum COMSIA model were satisfactory. Furthermore, analysis of the contour maps helped to provide guidelines for finding structural requirement. Therefore, the satisfactory results from this study may provide useful guidelines for drug development of anti-HCV inhibitors.

  3. Domain-Specific QSAR Models for Identifying Potential Estrogenic Activity of Phenols (FutureTox III)

    EPA Science Inventory

    Computational tools can be used for efficient evaluation of untested chemicals for their ability to disrupt the endocrine system. We have employed previously developed global QSAR models that were trained and validated on the ToxCast/Tox21 ER assay data for virtual screening of a...

  4. Comparison of 3D quantitative structure-activity relationship methods: Analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis

    NASA Astrophysics Data System (ADS)

    Woolfrey, John R.; Avery, Mitchell A.; Doweyko, Arthur M.

    1998-03-01

    Two three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and hypothetical active site lattice (HASL), were compared with respect to the analysis of a training set of 154 artemisinin analogues. Five models were created, including a complete HASL and two trimmed versions, as well as two CoMFA models (leave-one-out standard CoMFA and the guided-region selection protocol). Similar r2 and q2 values were obtained by each method, although some striking differences existed between CoMFA contour maps and the HASL output. Each of the four predictive models exhibited a similar ability to predict the activity of a test set of 23 artemisinin analogues, although some differences were noted as to which compounds were described well by either model.

  5. A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D- QSAR analysis, molecular docking, density functional and molecular dynamics simulation study.

    PubMed

    Choubey, Sanjay K; Jeyaraman, Jeyakanthan

    2016-11-01

    Deregulated epigenetic activity of Histone deacetylase 1 (HDAC1) in tumor development and carcinogenesis pronounces it as promising therapeutic target for cancer treatment. HDAC1 has recently captured the attention of researchers owing to its decisive role in multiple types of cancer. In the present study a multistep framework combining ligand based 3D-QSAR, molecular docking and Molecular Dynamics (MD) simulation studies were performed to explore potential compound with good HDAC1 binding affinity. Four different pharmacophore hypotheses Hypo1 (AADR), Hypo2 (AAAH), Hypo3 (AAAR) and Hypo4 (ADDR) were obtained. The hypothesis Hypo1 (AADR) with two hydrogen bond acceptors (A), one hydrogen bond donor (D) and one aromatics ring (R) was selected to build 3D-QSAR model on the basis of statistical parameter. The pharmacophore hypothesis produced a statistically significant QSAR model, with co-efficient of correlation r 2 =0.82 and cross validation correlation co-efficient q 2 =0.70. External validation result displays high predictive power with r 2 (o) value of 0.88 and r 2 (m) value of 0.58 to carry out further in silico studies. Virtual screening result shows ZINC70450932 as the most promising lead where HDAC1 interacts with residues Asp99, His178, Tyr204, Phe205 and Leu271 forming seven hydrogen bonds. A high docking score (-11.17kcal/mol) and lower docking energy -37.84kcal/mol) displays the binding efficiency of the ligand. Binding free energy calculation was done using MM/GBSA to access affinity of ligands towards protein. Density Functional Theory was employed to explore electronic features of the ligands describing intramolcular charge transfer reaction. Molecular dynamics simulation studies at 50ns display metal ion (Zn)-ligand interaction which is vital to inhibit the enzymatic activity of the protein. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. (Q)SAR studies to design new human choline kinase inhibitors as antiproliferative drugs.

    PubMed

    Campos, J M; Sánchez-Martín, R M; Conejo-García, A; Entrena, A; Gallo, M A; Espinosa, A

    2006-01-01

    Most of the signal transduction pathways are mediated by protein kinases regulating every aspect of cell function. Mutations which deregulate their expression or their function or both result in cancers. Therefore, protein kinase inhibitors have become the focus of development of new therapies for cancer. A comprehensive review of Choline kinase (ChoK) was published by us in 2003. Since then, molecular information of ChoK inhibitors has been accumulated. In this review, we intend to summarize the new lines of evidence that will include the design of the most active antiproliferative agents so far described against ChoK. Studies have been aimed at the establishment of structure-activity relationships and the structural parameters that define ChoK inhibitory and antiproliferative activities of a set of twenty-five acyclic biscationic pyridophane and forty acyclic biscationic quinolinephane compounds. The corresponding QSAR equation was obtained for the whole set of bisquinolinium compounds for the antiproliferative activity, taking into consideration the electronic parameter sigma(R) of R(4), the molar refractivity (MR) of R(8), and the lipophilic parameters clog P and pi(linker). The most potent antiproliferative agent shows an IC(50) = 0.45 microM, predicted by the QSAR equation, whilst its experimental value is IC(50) = 0.20 microM. Finally, toxicity assays were performed for the most promising compounds because of their interesting antiproliferative activities [IC(50 HT-29) = 0.70, 0.80, 1.50 and 1.90 microM] and low toxicity [LD(50) = 16.7, 12.5, > 25 and > 20 mg/kg of mouse]. These biological activities justify further analysis for antitumoral assays under in vivo conditions.

  7. Molecular description of α-keto-based inhibitors of cruzain with activity against Chagas disease combining 3D-QSAR studies and molecular dynamics.

    PubMed

    Saraiva, Ádria P B; Miranda, Ricardo M; Valente, Renan P P; Araújo, Jéssica O; Souza, Rutelene N B; Costa, Clauber H S; Oliveira, Amanda R S; Almeida, Michell O; Figueiredo, Antonio F; Ferreira, João E V; Alves, Cláudio Nahum; Honorio, Kathia M

    2018-04-22

    In this work, a group of α-keto-based inhibitors of the cruzain enzyme with anti-chagas activity was selected for a three-dimensional quantitative structure-activity relationship study (3D-QSAR) combined with molecular dynamics (MD). Firstly, statistical models based on Partial Least Square (PLS) regression were developed employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) descriptors. Validation parameters (q 2 and r 2 )for the models were, respectively, 0.910 and 0.997 (CoMFA) and 0.913 and 0.992 (CoMSIA). In addition, external validation for the models using a test group revealed r 2 pred  = 0.728 (CoMFA) and 0.971 (CoMSIA). The most relevant aspect in this study was the generation of molecular fields in both favorable and unfavorable regions based on the models developed. These fields are important to interpret modifications necessary to enhance the biological activities of the inhibitors. This analysis was restricted considering the inhibitors in a fixed conformation, not interacting with their target, the cruzain enzyme. Then, MD was employed taking into account important variables such as time and temperature. MD helped describe the behavior of the inhibitors and their properties showed similar results as those generated by QSAR-3D study. © 2018 John Wiley & Sons A/S.

  8. Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions.

    PubMed

    Du, Q S; Ma, Y; Xie, N Z; Huang, R B

    2014-01-01

    In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

  9. (Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.

    PubMed

    Van Bossuyt, Melissa; Van Hoeck, Els; Raitano, Giuseppa; Manganelli, Serena; Braeken, Els; Ates, Gamze; Vanhaecke, Tamara; Van Miert, Sabine; Benfenati, Emilio; Mertens, Birgit; Rogiers, Vera

    2017-04-01

    Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics, …) have triggered the need for an efficient screening strategy to prioritize the substances of highest concern. In this context, alternative methods such as in silico (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned 'high priority' as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Synthesis, biological evaluation, and 3D QSAR study of 2-methyl-4-oxo-3-oxetanylcarbamic acid esters as N-acylethanolamine acid amidase (NAAA) inhibitors.

    PubMed

    Ponzano, Stefano; Berteotti, Anna; Petracca, Rita; Vitale, Romina; Mengatto, Luisa; Bandiera, Tiziano; Cavalli, Andrea; Piomelli, Daniele; Bertozzi, Fabio; Bottegoni, Giovanni

    2014-12-11

    N-(2-Oxo-3-oxetanyl)carbamic acid esters have recently been reported to be noncompetitive inhibitors of the N-acylethanolamine acid amidase (NAAA) potentially useful for the treatment of pain and inflammation. In the present study, we further explored the structure-activity relationships of the carbamic acid ester side chain of 2-methyl-4-oxo-3-oxetanylcarbamic acid ester derivatives. Additional favorable features in the design of potent NAAA inhibitors have been found together with the identification of a single digit nanomolar inhibitor. In addition, we devised a 3D QSAR using the atomic property field method. The model turned out to be able to account for the structural variability and was prospectively validated by designing, synthesizing, and testing novel inhibitors. The fairly good agreement between predictions and experimental potency values points to this 3D QSAR model as the first example of quantitative structure-activity relationships in the field of NAAA inhibitors.

  11. A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

    PubMed

    Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

    2015-05-30

    A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

  12. Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

    NASA Technical Reports Server (NTRS)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Jewell, A. D.; Zhou, H.; Manatt, K.; Kisor, A. K.

    2005-01-01

    We report a Quantitative Structure-Activity Relationships (QSAR) study using Genetic Function Approximations (GFA) to describe the polymer-carbon composite sensor activities in the JPL Electronic Nose, when exposed to chemical vapors at parts-per-million concentration levels.

  13. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kiwamoto, R., E-mail: reiko.kiwamoto@wur.nl; Spenkelink, A.; Rietjens, I.M.C.M.

    Acyclic α,β-unsaturated aldehydes present in food raise a concern because the α,β-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,β-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure–activity relationships (QSARs) defined with a training set of sixmore » selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment. - Highlights: • Physiologically based in silico models were made for 18 α,β-unsaturated aldehydes. • Kinetic parameters were determined by in vitro incubations and a QSAR approach. • DNA adduct formation was negligible at levels relevant for dietary intake. • The use of QSAR-based PBK/D modelling facilitates group evaluations and read-across.« less

  14. 20180318 - Automated workflows for data curation and standardization of chemical structures for QSAR modeling (ACS Spring)

    EPA Science Inventory

    Large collections of chemical structures and associated experimental data are publicly available, and can be used to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experime...

  15. Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

    PubMed Central

    MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

    2017-01-01

    Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

  16. Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

    PubMed

    MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

    2017-01-01

    Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

  17. 3D-QSAR and virtual screening studies of thiazolidine-2,4-dione analogs: Validation of experimental inhibitory potencies towards PIM-1 kinase

    NASA Astrophysics Data System (ADS)

    Asati, Vivek; Bharti, Sanjay Kumar; Budhwani, Ashok Kumar

    2017-04-01

    The proviral insertion site in moloney murine leukemia virus (PIM) is a family of serine/threonine kinase of Ca2+-calmodulin-dependent protein kinase (CAMK) group which is responsible for the activation and regulation of cellular transcription and translation. The three isoforms of PIM kinase (PIM-1, PIM-2 and PIM-3) share high homology and functional idleness are widely expressed and involved in a variety of biological processes including cell survival, proliferation, differentiation and apoptosis. Altered expression of PIM-1 kinase correlated with hematologic malignancies and solid tumors. In the present study, atom-based 3D-QSAR, docking and virtual screening studies have been performed on a series of thiazolidine-2,4-dione derivatives as PIM-1 kinase inhibitors. 3D-QSAR and docking approach has shortlisted the most active thiazolidine-2,4-dione derivatives such as 28, 31, 33 and 35 with the incorporation of more than one structural feature in a single molecule. External validations by various parameters and molecular docking studies at the active site of PIM-1 kinase have proved the reliability of the developed 3D-QSAR model. The generated pharmacophore (AADHR.33) from 3D-QSAR study was used for screening of drug like compounds from ZINC database, where ZINC15056464 and ZINC83292944 showed potential binding affinities at the active site amino acid residues (LYS67, GLU171, ASP128 and ASP186) of PIM-1 kinase.

  18. Investigation into adamantane-based M2 inhibitors with FB-QSAR.

    PubMed

    Wei, Hang; Wang, Cheng-Hua; Du, Qi-Shi; Meng, Jianzong; Chou, Kuo-Chen

    2009-07-01

    Because of their high resistance rate to the existing drugs, influenza A viruses have become a threat to human beings. It is known that the replication of influenza A viruses needs a pH-gated proton channel, the so-called M2 channel. Therefore, to develop effective drugs against influenza A, the most logic strategy is to inhibit the M2 channel. Recently, the atomic structure of the M2 channel was determined by NMR spectroscopy (Schnell, J.R. and Chou, J.J., Nature, 2008, 451, 591-595). The high-resolution NMR structure has provided a solid basis for structure-based drug design approaches. In this study, a benchmark dataset has been constructed that contains 34 newly-developed adamantane-based M2 inhibitors and covers considerable structural diversities and wide range of bioactivities. Based on these compounds, an in-depth analysis was performed with the newly developed fragment-based quantitative structure-activity relationship (FB-QSAR) algorithm. The results thus obtained provide useful insights for dealing with the drug-resistant problem and designing effective adamantane-based antiflu drugs.

  19. Biochemical interpretation of quantitative structure-activity relationships (QSAR) for biodegradation of N-heterocycles: a complementary approach to predict biodegradability.

    PubMed

    Philipp, Bodo; Hoff, Malte; Germa, Florence; Schink, Bernhard; Beimborn, Dieter; Mersch-Sundermann, Volker

    2007-02-15

    Prediction of the biodegradability of organic compounds is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. We combined quantitative structure-activity relationships (QSAR) with the systematic collection of biochemical knowledge to establish rules for the prediction of aerobic biodegradation of N-heterocycles. Validated biodegradation data of 194 N-heterocyclic compounds were analyzed using the MULTICASE-method which delivered two QSAR models based on 17 activating (OSAR 1) and on 16 inactivating molecular fragments (GSAR 2), which were statistically significantly linked to efficient or poor biodegradability, respectively. The percentages of correct classifications were over 99% for both models, and cross-validation resulted in 67.9% (GSAR 1) and 70.4% (OSAR 2) correct predictions. Biochemical interpretation of the activating and inactivating characteristics of the molecular fragments delivered plausible mechanistic interpretations and enabled us to establish the following biodegradation rules: (1) Target sites for amidohydrolases and for cytochrome P450 monooxygenases enhance biodegradation of nonaromatic N-heterocycles. (2) Target sites for molybdenum hydroxylases enhance biodegradation of aromatic N-heterocycles. (3) Target sites for hydratation by an urocanase-like mechanism enhance biodegradation of imidazoles. Our complementary approach represents a feasible strategy for generating concrete rules for the prediction of biodegradability of organic compounds.

  20. Side-chain conformational space analysis (SCSA): A multi conformation-based QSAR approach for modeling and prediction of protein-peptide binding affinities

    NASA Astrophysics Data System (ADS)

    Zhou, Peng; Chen, Xiang; Shang, Zhicai

    2009-03-01

    In this article, the concept of multi conformation-based quantitative structure-activity relationship (MCB-QSAR) is proposed, and based upon that, we describe a new approach called the side-chain conformational space analysis (SCSA) to model and predict protein-peptide binding affinities. In SCSA, multi-conformations (rather than traditional single-conformation) have received much attention, and the statistical average information on multi-conformations of side chains is determined using self-consistent mean field theory based upon side chain rotamer library. Thereby, enthalpy contributions (including electrostatic, steric, hydrophobic interaction and hydrogen bond) and conformational entropy effects to the binding are investigated in terms of occurrence probability of residue rotamers. Then, SCSA was applied into the dataset of 419 HLA-A*0201 binding peptides, and nonbonding contributions of each position in peptide ligands are well determined. For the peptides, the hydrogen bond and electrostatic interactions of the two ends are essential to the binding specificity, van der Waals and hydrophobic interactions of all the positions ensure strong binding affinity, and the loss of conformational entropy at anchor positions partially counteracts other favorable nonbonding effects.

  1. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

    PubMed

    Dobchev, Dimitar; Karelson, Mati

    2016-07-01

    Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

  2. QSAR and docking based semi-synthesis and in vitro evaluation of 18 β-glycyrrhetinic acid derivatives against human lung cancer cell line A-549.

    PubMed

    Yadav, Dharmendra Kumar; Kalani, Komal; Khan, Feroz; Srivastava, Santosh Kumar

    2013-12-01

    For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r(2)) and prediction accuracy (rCV(2)) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.

  3. Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1.

    PubMed

    Gonzalez, J; Marchand-Geneste, N; Giraudel, J L; Shimada, T

    2012-01-01

    To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.

  4. The 3D Structure of the Binding Pocket of the Human Oxytocin Receptor for Benzoxazine Antagonists, Determined by Molecular Docking, Scoring Functions and 3D-QSAR Methods

    NASA Astrophysics Data System (ADS)

    Jójárt, Balázs; Martinek, Tamás A.; Márki, Árpád

    2005-05-01

    Molecular docking and 3D-QSAR studies were performed to determine the binding mode for a series of benzoxazine oxytocin antagonists taken from the literature. Structural hypotheses were generated by docking the most active molecule to the rigid receptor by means of AutoDock 3.05. The cluster analysis yielded seven possible binding conformations. These structures were refined by using constrained simulated annealing, and the further ligands were aligned in the refined receptor by molecular docking. A good correlation was found between the estimated Δ G bind and the p K i values for complex F. The Connolly-surface analysis, CoMFA and CoMSIA models q CoMFA 2 = 0.653, q CoMSA 2 = 0.630 and r pred,CoMFA 2 = 0.852 , r pred,CoMSIA 2 = 0.815) confirmed the scoring function results. The structural features of the receptor-ligand complex and the CoMFA and CoMSIA fields are in closely connected. These results suggest that receptor-ligand complex F is the most likely binding hypothesis for the studied benzoxazine analogs.

  5. Eco-friendly synthesis, in vitro anti-proliferative evaluation, and 3D-QSAR analysis of a novel series of monocationic 2-aryl/heteroaryl-substituted 6-(2-imidazolinyl)benzothiazole mesylates.

    PubMed

    Racané, Livio; Ptiček, Lucija; Sedić, Mirela; Grbčić, Petra; Kraljević Pavelić, Sandra; Bertoša, Branimir; Sović, Irena; Karminski-Zamola, Grace

    2018-04-17

    positively correlated with anti-proliferative activity, while compound's capability to accept H-bond was identified as a negatively correlated property. Comparison of molecular properties identified for different cell lines enabled assumptions about similarity of mode of action through which anti-proliferative activities against different cell lines are accomplished. Novel compounds that are predicted to have enhanced activities in comparison with herein presented ones were designed using 3D-QSAR analysis as guideline.

  6. Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach.

    PubMed

    Gadhe, Changdev G; Kothandan, Gugan; Cho, Seung Joo

    2013-01-01

    Chemokine receptor 5 (CCR5) is an important receptor used by human immunodeficiency virus type 1 (HIV-1) to gain viral entry into host cell. In this study, we used a combined approach of comparative modeling, molecular docking, and three dimensional quantitative structure activity relationship (3D-QSAR) analyses to elucidate detailed interaction of CCR5 with their inhibitors. Docking study of the most potent inhibitor from a series of compounds was done to derive the bioactive conformation. Parameters such as random selection, rational selection, different charges and grid spacing were utilized in the model development to check their performance on the model predictivity. Final comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were chosen based on the rational selection method, Gasteiger-Hückel charges and a grid spacing of 0.5 Å. Rational model for CoMFA (q(2) = 0.722, r(2) = 0.884, Q(2) = 0.669) and CoMSIA (q(2) = 0.712, r(2) = 0.825, Q(2) = 0.522) was obtained with good statistics. Mapping of contour maps onto CCR5 interface led us to better understand of the ligand-protein interaction. Docking analysis revealed that the Glu283 is crucial for interaction. Two new amino acid residues, Tyr89 and Thr167 were identified as important in ligand-protein interaction. No site directed mutagenesis studies on these residues have been reported.

  7. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Valencia, Antoni; Prous, Josep; Mora, Oscar

    As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90%more » was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation

  8. 3D-QSAR comparative molecular field analysis on opioid receptor antagonists: pooling data from different studies.

    PubMed

    Peng, Youyi; Keenan, Susan M; Zhang, Qiang; Kholodovych, Vladyslav; Welsh, William J

    2005-03-10

    Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molecular field analysis (CoMFA) on a series of opioid receptor antagonists. To obtain statistically significant and robust CoMFA models, a sizable data set of naltrindole and naltrexone analogues was assembled by pooling biological and structural data from independent studies. A process of "leave one data set out", similar to the traditional "leave one out" cross-validation procedure employed in partial least squares (PLS) analysis, was utilized to study the feasibility of pooling data in the present case. These studies indicate that our approach yields statistically significant and highly predictive CoMFA models from the pooled data set of delta, mu, and kappa opioid receptor antagonists. All models showed excellent internal predictability and self-consistency: q(2) = 0.69/r(2) = 0.91 (delta), q(2) = 0.67/r(2) = 0.92 (mu), and q(2) = 0.60/r(2) = 0.96 (kappa). The CoMFA models were further validated using two separate test sets: one test set was selected randomly from the pooled data set, while the other test set was retrieved from other published sources. The overall excellent agreement between CoMFA-predicted and experimental binding affinities for a structurally diverse array of ligands across all three opioid receptor subtypes gives testimony to the superb predictive power of these models. CoMFA field analysis demonstrated that the variations in binding affinity of opioid antagonists are dominated by steric rather than electrostatic interactions with the three opioid receptor binding sites. The CoMFA steric-electrostatic contour maps corresponding to the delta, mu, and kappa opioid receptor subtypes reflected the characteristic similarities and differences in the familiar "message-address" concept of opioid receptor ligands. Structural modifications to increase selectivity for the delta over mu and kappa opioid receptors have been predicted on the

  9. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modeling

    EPA Science Inventory

    Increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associat...

  10. Synthesis, fungicidal evaluation and 3D-QSAR studies of novel 1,3,4-thiadiazole xylofuranose derivatives

    PubMed Central

    Zong, Guanghui; Yan, Xiaojing; Bi, Jiawei; Jiang, Rui; Qin, Yinan; Yuan, Huizhu; Lu, Huizhe; Dong, Yanhong; Jin, Shuhui; Zhang, Jianjun

    2017-01-01

    1,3,4-Thiadiazole and sugar-derived molecules have proven to be promising agrochemicals with growth promoting, insecticidal and fungicidal activities. In the research field of agricultural fungicide, applying union of active group we synthesized a new set of 1,3,4-thiadiazole xylofuranose derivatives and all of the compounds were characterized by 1H NMR and HRMS. In precise toxicity measurement, some of compounds exhibited more potent fungicidal activities than the most widely used commercial fungicide Chlorothalonil, promoting further research and development. Based on our experimental data, 3D-QSAR (three-dimensional quantitative structure-activity relationship) was established and investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques, helping to better understand the structural requirements of lead compounds with high fungicidal activity and environmental compatibility. PMID:28746366

  11. Novel dimer based descriptors with solvational computation for QSAR study of oxadiazoylbenzoyl-ureas as novel insect-growth regulators.

    PubMed

    Fan, Feng; Cheng, Jiagao; Li, Zhong; Xu, Xiaoyong; Qian, Xuhong

    2010-02-01

    Molecular aggregation state of bioactive compounds plays a key role in its bio-interactive procedure. In this article, based on the structure information of dimers, the simplest model of molecular aggregation state, and combined with solvational computation, total four descriptors (DeltaV, MR2, DeltaE(1), and DeltaE(2)) were calculated for QSAR study of a novel insect-growth regulator, N-(5-phenyl-1,3,4-oxadiazol-2-yl)-N'-benzoyl urea. Two QSAR models were constructed with r(2) = 0.671, q(2) = 0.516 and r(2) = 0.816, q(2) = 0.695, respectively. It implicates that the bioactivity may strongly depend on the characters of molecular aggregation state, especially on the dimeric transport ability from oil phase to water phase. Copyright 2009 Wiley Periodicals, Inc.

  12. Obscure phenomena in statistical analysis of quantitative structure-activity relationships. Part 1: Multicollinearity of physicochemical descriptors.

    PubMed

    Mager, P P; Rothe, H

    1990-10-01

    Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.

  13. Pharmacophore Based 3D-QSAR, Virtual Screening and Docking Studies on Novel Series of HDAC Inhibitors with Thiophen Linker as Anticancer Agents.

    PubMed

    Patel, Preeti; Singh, Avineesh; Patel, Vijay K; Jain, Deepak K; Veerasamy, Ravichandran; Rajak, Harish

    2016-01-01

    Histone deacetylase (HDAC) inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. To identify the important pharmacophoric features and correlate 3Dchemical structure with biological activity using 3D-QSAR and Pharmacophore modeling studies. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with wellassigned HDAC inhibitory activity were used for 3D-QSAR model development. Best 3D-QSAR model, which is a five partial least square (PLS) factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811), cross-validated coefficient rcv 2=0.9807 and R2 pred=0.7147 with low standard deviation (0.0952). Additionally, the selected pharmacophore model DDRRR.419 was used as a 3D query for virtual screening against the ZINC database. In the virtual screening workflow, docking studies (HTVS, SP and XP) were carried out by selecting multiple receptors (PDB ID: 1T69, 1T64, 4LXZ, 4LY1, 3MAX, 2VQQ, 3C10, 1W22). Finally, six compounds were obtained based on high scoring function (dock score -11.2278-10.2222 kcal/mol) and diverse structures. The structure activity correlation was established using virtual screening, docking, energetic based pharmacophore modelling, pharmacophore, atom based 3D QSAR models and their validation. The outcomes of these studies could be further employed for the design of novel HDAC inhibitors for anticancer activity.

  14. QSAR by LFER model of cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives using principal component factor analysis and genetic function approximation.

    PubMed

    Roy, Kunal; Leonard, J Thomas

    2005-04-15

    Cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives were subjected to quantitative structure-activity relationship (QSAR) study using linear free energy related (LFER) model of Hansch using electronic (Hammett sigma), hydrophobicity (pi) and steric (molar refractivity and STERIMOL L, B1, B2, B3 and B4) parameters of phenyl ring substituents of the compounds, along with appropriate indicator variables. Principal component factor analysis (FA) was used as the data-preprocessing step to identify the important predictor variables contributing to the response variable and to avoid collinearities among them. The generated multiple linear regression (MLR) equations were statistically validated using leave-one-out technique. Genetic function approximation (GFA) was also used on the same data set to develop QSAR equations, which produced the same best equation as obtained with FA-MLR. The final equation is of acceptable statistical quality (explained variance 80.2%) and predictive potential (leave-one-out predicted variance 74%). The analysis explores the structural and physicochemical contributions of the compounds for cytotoxicity. A thiol substituent at 2 position of the imidazole nucleus decreases cytotoxicity when compared to the corresponding unsubstituted congener. Presence of hydrogen bond donor group at meta position of the phenyl ring present at 5 position of the imidazole nucleus also reduces cytotoxicity. Additionally, absence of any substituent at 2 and 3 positions of the phenyl ring of 1-phenylamino fragment reduces the cytotoxicity. The negative coefficient of sigmap indicates that presence of electron-withdrawing substituents at the para position of the phenyl ring of the 1-phenylamino fragment is not favourable for the cytotoxicity. Again, lipophilicity of meta substituents of the 5-phenyl ring increases cytotoxicity. The coefficients of molar refractivity (MRm) and STERIMOL parameters for meta substituents (Lm, B1m and B4m) of the

  15. General baseline toxicity QSAR for nonpolar, polar and ionisable chemicals and their mixtures in the bioluminescence inhibition assay with Aliivibrio fischeri.

    PubMed

    Escher, Beate I; Baumer, Andreas; Bittermann, Kai; Henneberger, Luise; König, Maria; Kühnert, Christin; Klüver, Nils

    2017-03-22

    The Microtox assay, a bioluminescence inhibition assay with the marine bacterium Aliivibrio fischeri, is one of the most popular bioassays for assessing the cytotoxicity of organic chemicals, mixtures and environmental samples. Most environmental chemicals act as baseline toxicants in this short-term screening assay, which is typically run with only 30 min of exposure duration. Numerous Quantitative Structure-Activity Relationships (QSARs) exist for the Microtox assay for nonpolar and polar narcosis. However, typical water pollutants, which have highly diverse structures covering a wide range of hydrophobicity and speciation from neutral to anionic and cationic, are often outside the applicability domain of these QSARs. To include all types of environmentally relevant organic pollutants we developed a general baseline toxicity QSAR using liposome-water distribution ratios as descriptors. Previous limitations in availability of experimental liposome-water partition constants were overcome by reliable prediction models based on polyparameter linear free energy relationships for neutral chemicals and the COSMOmic model for charged chemicals. With this QSAR and targeted mixture experiments we could demonstrate that ionisable chemicals fall in the applicability domain. Most investigated water pollutants acted as baseline toxicants in this bioassay, with the few outliers identified as uncouplers or reactive toxicants. The main limitation of the Microtox assay is that chemicals with a high melting point and/or high hydrophobicity were outside of the applicability domain because of their low water solubility. We quantitatively derived a solubility cut-off but also demonstrated with mixture experiments that chemicals inactive on their own can contribute to mixture toxicity, which is highly relevant for complex environmental mixtures, where these chemicals may be present at concentrations below the solubility cut-off.

  16. First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington's disease: A proposal to chemists!

    PubMed

    Amin, Sk Abdul; Adhikari, Nilanjan; Jha, Tarun; Gayen, Shovanlal

    2016-12-01

    Huntington's disease (HD) is caused by mutation of huntingtin protein (mHtt) leading to neuronal cell death. The mHtt induced toxicity can be rescued by inhibiting the kynurenine monooxygenase (KMO) enzyme. Therefore, KMO is a promising drug target to address the neurodegenerative disorders such as Huntington's diseases. Fiftysix arylpyrimidine KMO inhibitors are structurally explored through regression and classification based multi-QSAR modeling, pharmacophore mapping and molecular docking approaches. Moreover, ten new compounds are proposed and validated through the modeling that may be effective in accelerating Huntington's disease drug discovery efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides

    NASA Astrophysics Data System (ADS)

    Tian, Feifei; Zhou, Peng; Li, Zhiliang

    2007-03-01

    In this paper, a new topological descriptor T-scale is derived from principal component analysis (PCA) on the collected 67 kinds of structural and topological variables of 135 amino acids. Applying T-scale to three peptide panels as 58 angiotensin-converting enzyme (ACE) inhibitors, 20 thromboplastin inhibitors (TI) and 28 bovine lactoferricin-(17-31)-pentadecapeptides (LFB), the resulting QSAR models, constructed by partial least squares (PLS), are all superior to reference reports, with correlative coefficient r2 and cross-validated q2 of 0.845, 0.786; 0.996, 0.782 (0.988, 0.961); 0.760, 0.627, respectively.

  18. INFLUENCE OF MATRIX FORMULATION ON DERMAL PERCUTANEOUS ABSORPTION OF TRIAZOLE FUNGICIDES USING QSAR AND PBPK / PD MODELS

    EPA Science Inventory

    The objective of this work is to use the Exposure Related Dose Estimating Model (ERDEM) and quantitative structure-activity relationship (QSAR) models to develop an assessment tool for human exposure assessment to triazole fungicides. A dermal exposure route is used for the physi...

  19. Designing of phenol-based β-carbonic anhydrase1 inhibitors through QSAR, molecular docking, and MD simulation approach.

    PubMed

    Ahamad, Shahzaib; Hassan, Md Imtaiyaz; Dwivedi, Neeraja

    2018-05-01

    Tuberculosis (Tb) is an airborne infectious disease caused by Mycobacterium tuberculosis. Beta-carbonic anhydrase 1 ( β-CA1 ) has emerged as one of the potential targets for new antitubercular drug development. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking, and molecular dynamics (MD) simulation approaches were performed on a series of natural and synthetic phenol-based β-CA1 inhibitors. The developed 3D-QSAR model ( r 2  = 0.94, q 2  = 0.86, and pred_r 2  = 0.74) indicated that the steric and electrostatic factors are important parameters to modulate the bioactivity of phenolic compounds. Based on this indication, we designed 72 new phenolic inhibitors, out of which two compounds (D25 and D50) effectively stabilized β-CA1 receptor and, thus, are potential candidates for new generation antitubercular drug discovery program.

  20. 3D-QSAR, homology modeling, and molecular docking studies on spiropiperidines analogues as agonists of nociceptin/orphanin FQ receptor.

    PubMed

    Liu, Ming; He, Lin; Hu, Xiaopeng; Liu, Peiqing; Luo, Hai-Bin

    2010-12-01

    The nociceptin/orphanin FQ receptor (NOP) has been implicated in a wide range of biological functions, including pain, anxiety, depression and drug abuse. Especially, its agonists have a great potential to be developed into anxiolytics. However, the crystal structure of NOP is still not available. In the present work, both structure-based and ligand-based modeling methods have been used to achieve a comprehensive understanding on 67N-substituted spiropiperidine analogues as NOP agonists. The comparative molecular-field analysis method was performed to formulate a reasonable 3D-QSAR model (cross-validated coefficient q(2)=0.819 and conventional r(2)=0.950), whose robustness and predictability were further verified by leave-eight-out, Y-randomization, and external test-set validations. The excellent performance of CoMFA to the affinity differences among these compounds was attributed to the contributions of electrostatic/hydrogen-bonding and steric/hydrophobic interactions, which was supported by the Surflex-Dock and CDOCKER molecular-docking simulations based on the 3D model of NOP built by the homology modeling method. The CoMFA contour maps and the molecular docking simulations were integrated to propose a binding mode for the spiropiperidine analogues at the binding site of NOP. Copyright © 2010 Elsevier Ltd. All rights reserved.

  1. A combined pharmacophore modeling, 3D-QSAR and molecular docking study of substituted bicyclo-[3.3.0]oct-2-enes as liver receptor homolog-1 (LRH-1) agonists

    NASA Astrophysics Data System (ADS)

    Lalit, Manisha; Gangwal, Rahul P.; Dhoke, Gaurao V.; Damre, Mangesh V.; Khandelwal, Kanchan; Sangamwar, Abhay T.

    2013-10-01

    A combined pharmacophore modelling, 3D-QSAR and molecular docking approach was employed to reveal structural and chemical features essential for the development of small molecules as LRH-1 agonists. The best HypoGen pharmacophore hypothesis (Hypo1) consists of one hydrogen-bond donor (HBD), two general hydrophobic (H), one hydrophobic aromatic (HYAr) and one hydrophobic aliphatic (HYA) feature. It has exhibited high correlation coefficient of 0.927, cost difference of 85.178 bit and low RMS value of 1.411. This pharmacophore hypothesis was cross-validated using test set, decoy set and Cat-Scramble methodology. Subsequently, validated pharmacophore hypothesis was used in the screening of small chemical databases. Further, 3D-QSAR models were developed based on the alignment obtained using substructure alignment. The best CoMFA and CoMSIA model has exhibited excellent rncv2 values of 0.991 and 0.987, and rcv2 values of 0.767 and 0.703, respectively. CoMFA predicted rpred2 of 0.87 and CoMSIA predicted rpred2 of 0.78 showed that the predicted values were in good agreement with the experimental values. Molecular docking analysis reveals that π-π interaction with His390 and hydrogen bond interaction with His390/Arg393 is essential for LRH-1 agonistic activity. The results from pharmacophore modelling, 3D-QSAR and molecular docking are complementary to each other and could serve as a powerful tool for the discovery of potent small molecules as LRH-1 agonists.

  2. QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity.

    PubMed

    Gomes, Marcelo N; Braga, Rodolpho C; Grzelak, Edyta M; Neves, Bruno J; Muratov, Eugene; Ma, Rui; Klein, Larry L; Cho, Sanghyun; Oliveira, Guilherme R; Franzblau, Scott G; Andrade, Carolina Horta

    2017-09-08

    New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  3. The QSAR and docking calculations of fullerene derivatives as HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Saleh, Noha A.

    2015-02-01

    The inhibition of HIV-1 protease is considered as one of the most important targets for drug design and the deactivation of HIV-1. In the present work, the fullerene surface (C60) is modified by adding oxygen atoms as well as hydroxymethylcarbonyl (HMC) groups to form 6 investigated fullerene derivative compounds. These compounds have one, two, three, four or five O atoms + HMC groups at different positions on phenyl ring. The effect of the repeating of these groups on the ability of suggested compounds to inhibit the HIV protease is studied by calculating both Quantitative Structure Activity Relationship (QSAR) properties and docking simulation. Based on the QSAR descriptors, the solubility and the hydrophilicity of studied fullerene derivatives increased with increasing the number of oxygen atoms + HMC groups in the compound. While docking calculations indicate that, the compound with two oxygen atoms + HMC groups could interact and binds with HIV-1 protease active site. This is could be attributed to the active site residues of HIV-1 protease are hydrophobic except the two aspartic acids. So that, the increase in the hydrophilicity and polarity of the compound is preventing and/or decreasing the hydrophobic interaction between the compound and HIV-1 protease active site.

  4. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  5. An examination of data quality on QSAR Modeling in regards to the environmental sciences (UNC-CH talk)

    EPA Science Inventory

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to...

  6. Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds.

    PubMed

    Fu, Zhiqiang; Chen, Jingwen; Li, Xuehua; Wang, Ya'nan; Yu, Haiying

    2016-04-01

    The octanol-air partition coefficient (KOA) is needed for assessing multimedia transport and bioaccumulability of organic chemicals in the environment. As experimental determination of KOA for various chemicals is costly and laborious, development of KOA estimation methods is necessary. We investigated three methods for KOA prediction, conventional quantitative structure-activity relationship (QSAR) models based on molecular structural descriptors, group contribution models based on atom-centered fragments, and a novel model that predicts KOA via solvation free energy from air to octanol phase (ΔGO(0)), with a collection of 939 experimental KOA values for 379 compounds at different temperatures (263.15-323.15 K) as validation or training sets. The developed models were evaluated with the OECD guidelines on QSAR models validation and applicability domain (AD) description. Results showed that although the ΔGO(0) model is theoretically sound and has a broad AD, the prediction accuracy of the model is the poorest. The QSAR models perform better than the group contribution models, and have similar predictability and accuracy with the conventional method that estimates KOA from the octanol-water partition coefficient and Henry's law constant. One QSAR model, which can predict KOA at different temperatures, was recommended for application as to assess the long-range transport potential of chemicals. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Esposito, Emilio Xavier, E-mail: emilio@exeResearch.com; The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, IL 60045; Hopfinger, Anton J., E-mail: hopfingr@gmail.com

    2015-10-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted frommore » the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. - Highlights: • Proposed toxicity mechanism of action for decorated nanotubes complexes • Discussion of the key molecular features for each endpoint's mechanism of action • Unique mechanisms of action for each of the six biological systems • Hypothesized mechanisms of action based on QSAR/QNAR predictive models.« less

  8. QSAR analyses of 3-(4-benzylpiperidin-1-yl)-N-phenylpropylamine derivatives as potent CCR5 antagonists.

    PubMed

    Roy, Kunal; Leonard, J Thomas

    2005-01-01

    CCR5 receptor binding affinity of a series of 3-(4-benzylpiperidin-1-yl)propylamine congeners was subjected to QSAR study using the linear free energy related (LFER) model of Hansch. Appropriate indicator variables encoding different group contributions and different physicochemical variables such as hydrophobicity (pi), electronic (Hammett sigma), and steric (molar refractivity, STERIMOL values) parameters of phenyl ring substituents of the compounds were used as predictor variables. The Hansch analysis explores the importance of the lipophilicity and electron-donating substituents for the binding affinity. However, this method could not give more insight into the structure-activity relationships because of the diverse molecular features in the data set. 3D-QSAR analyses of the same data set using Molecular Shape Analysis (MSA), Receptor Surface Analysis (RSA), and Molecular Field Analysis (MFA) techniques were also performed. The best model with acceptable statistical quality was derived from the MSA, which showed the importance of the relative negative charge (RNCG): substituents with a high RNCG value have more binding affinity than the unsubstituted piperidine and phenyl (R1 position) congeners. The relative negative charge surface area (RNCS) is detrimental (e.g. R2 = 3,4-Cl2) for the activity. An increase in the length of the molecule in the Z dimension (Lz) is conducive (e.g. R3 = sulfonylmorpholino), while an increase in the area of the molecular shadow in the XZ plane (Sxz) is detrimental (e.g. R1 = N-c-hexylmethyl-5-oxopyrrolidin-3-yl) for the binding affinity. The presence of a chiral center makes the molecule less active (e.g. R1 = N-methyl-5-oxopyrrolidin-3-yl). An increase in the van der Waals area, the molecular volume, and the difference between the volume of the individual molecule and the shape reference compound are conducive (e.g. R3 = (CH3)2NSO2-) for the binding affinity. Substituents with higher JursFPSA_2 values (fractional charged partial

  9. QSAR modeling of flotation collectors using principal components extracted from topological indices.

    PubMed

    Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R

    2002-01-01

    Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

  10. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...

  11. QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

    NASA Astrophysics Data System (ADS)

    Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

    2015-10-01

    Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients (rpred2) of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

  12. Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR.

    PubMed

    Tansel, Berrin; Lee, Mengshan; Tansel, Derya Z

    2013-08-15

    First order removal rates for 15 polyaromatic hydrocarbons (PAHs) in soil, sediments and mangrove leaves were compared in relation to the parameters used in fate transport analyses (i.e., octanol-water partition coefficient, organic carbon-water partition coefficient, solubility, diffusivity in water, HOMO-LUMO gap, molecular size, molecular aspect ratio). The quantitative structure activity relationships (QSAR) and quantitative structure property relationships (QSPR) showed that the rate of disappearance of PAHs is correlated with their diffusivities in water as well as molecular volumes in different media. Strong correlations for the rate of disappearance of PAHs in sediments could not be obtained in relation to most of the parameters evaluated. The analyses showed that the QSAR and QSPR correlations developed for removal rates of PAHs in soils would not be adequate for sediments and plant tissues. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. The influence of data curation on QSAR Modeling – examining issues of quality versus quantity of data (SOT)

    EPA Science Inventory

    The construction of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available ...

  14. Synthesis and QSAR study of novel α-methylene-γ-butyrolactone derivatives as antifungal agents.

    PubMed

    Wu, Yong-Ling; Wang, De-Long; Guo, En-Hui; Song, Shuang; Feng, Jun-Tao; Zhang, Xing

    2017-03-01

    Thirty-six new α-benzylidene-γ-lactone compounds based α-methylene-γ-butyrolactone substructure were prepared and characterized by spectroscopic analysis. All compounds were evaluated for antifungal activities in vitro against six plant pathogenic fungi and the half maximal inhibitory concentration (IC 50 ) against Botrytis cinerea and Colletotrichum lagenarium were investigated. Compounds 5c-3 and 5c-5 with the halogen atom exhibited excellent fungicidal activity against B. cinerea (IC 50 =22.91, 18.89μM). The structure-activity relationships (SARs) analysis indicated that the derivatives with electron-withdrawing substituents at the meta- or para-positions improves the activity. Via the heuristic method, the generated quantitative structure-activity relationship (QSAR) model (R 2 =0.961) revealed a strong correlation of antifungal activity against B. cinerea with molecular structures of these compounds. Meanwhile, the cytotoxicity of 20 representative derivatives was tested in the human tumor cells line (HepG2) and the hepatic L02 cells line, the result indicated that the synthesized compounds showed significant inhibitory activity and limited selectivity. Compound 5c-5 has the highest fungicidal activity with IC 50 =18.89μM (against B. cinerea.) but low cytotoxicity with IC 50 =35.4μM (against HepG2 cell line) and IC 50 =68.8μM (against Hepatic L02 cell line). These encouraging results can be providing an alternative, promising use of α-benzylidene-γ-lactone through the design and exploration of eco-friendly fungicides with low toxicity and high efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Synthesis, QSAR, and Molecular Dynamics Simulation of Amidino-substituted Benzimidazoles as Dipeptidyl Peptidase III Inhibitors.

    PubMed

    Rastija, Vesna; Agić, Dejan; Tomiš, Sanja; Nikolič, Sonja; Hranjec, Marijana; Grace, Karminski-Zamola; Abramić, Marija

    2015-01-01

    A molecular modeling study is performed on series of benzimidazol-based inhibitors of human dipeptidyl peptidase III (DPP III). An eight novel compounds were synthesized in excellent yields using green chemistry approach. This study is aimed to elucidate the structural features of benzimidazole derivatives required for antagonism of human DPP III activity using Quantitative Structure-Activity Relationship (QSAR) analysis, and to understand the mechanism of one of the most potent inhibitor binding into the active site of this enzyme, by molecular dynamics (MD) simulations. The best model obtained includes S3K and RDF045m descriptors which have explained 89.4 % of inhibitory activity. Depicted moiety for strong inhibition activity matches to the structure of most potent compound. MD simulation has revealed importance of imidazolinyl and phenyl groups in the mechanism of binding into the active site of human DPP III.

  16. Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

    PubMed

    Maganti, Lakshmi; Das, Sanjit Kumar; Mascarenhas, Nahren Manuel; Ghoshal, Nanda

    2011-10-01

    The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Short communication: cheminformatics analysis to identify predictors of antiviral drug penetration into the female genital tract.

    PubMed

    Thompson, Corbin G; Sedykh, Alexander; Nicol, Melanie R; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Kashuba, Angela D M

    2014-11-01

    The exposure of oral antiretroviral (ARV) drugs in the female genital tract (FGT) is variable and almost unpredictable. Identifying an efficient method to find compounds with high tissue penetration would streamline the development of regimens for both HIV preexposure prophylaxis and viral reservoir targeting. Here we describe the cheminformatics investigation of diverse drugs with known FGT penetration using cluster analysis and quantitative structure-activity relationships (QSAR) modeling. A literature search over the 1950-2012 period identified 58 compounds (including 21 ARVs and representing 13 drug classes) associated with their actual concentration data for cervical or vaginal tissue, or cervicovaginal fluid. Cluster analysis revealed significant trends in the penetrative ability for certain chemotypes. QSAR models to predict genital tract concentrations normalized to blood plasma concentrations were developed with two machine learning techniques utilizing drugs' molecular descriptors and pharmacokinetic parameters as inputs. The QSAR model with the highest predictive accuracy had R(2)test=0.47. High volume of distribution, high MRP1 substrate probability, and low MRP4 substrate probability were associated with FGT concentrations ≥1.5-fold plasma concentrations. However, due to the limited FGT data available, prediction performances of all models were low. Despite this limitation, we were able to support our findings by correctly predicting the penetration class of rilpivirine and dolutegravir. With more data to enrich the models, we believe these methods could potentially enhance the current approach of clinical testing.

  18. In Vitro Antioxidant Activity of Selected 4-Hydroxy-chromene-2-one Derivatives—SAR, QSAR and DFT Studies

    PubMed Central

    Mladenović, Milan; Mihailović, Mirjana; Bogojević, Desanka; Matić, Sanja; Nićiforović, Neda; Mihailović, Vladimir; Vuković, Nenad; Sukdolak, Slobodan; Solujić, Slavica

    2011-01-01

    The series of fifteen synthesized 4-hydroxycoumarin derivatives was subjected to antioxidant activity evaluation in vitro, through total antioxidant capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH), hydroxyl radical, lipid peroxide scavenging and chelating activity. The highest activity was detected during the radicals scavenging, with 2b, 6b, 2c, and 4c noticed as the most active. The antioxidant activity was further quantified by the quantitative structure-activity relationships (QSAR) studies. For this purpose, the structures were optimized using Paramethric Method 6 (PM6) semi-empirical and Density Functional Theory (DFT) B3LYP methods. Bond dissociation enthalpies of coumarin 4-OH, Natural Bond Orbital (NBO) gained hybridization of the oxygen, acidity of the hydrogen atom and various molecular descriptors obtained, were correlated with biological activity, after which we designed 20 new antioxidant structures, using the most favorable structural motifs, with much improved predicted activity in vitro. PMID:21686153

  19. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    EPA Science Inventory

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  20. Construction of 4D-QSAR Models for Use in the Design of Novel p38-MAPK Inhibitors

    NASA Astrophysics Data System (ADS)

    Romeiro, Nelilma Correia; Albuquerque, Magaly Girão; de Alencastro, Ricardo Bicca; Ravi, Malini; Hopfinger, Anton J.

    2005-06-01

    The p38-mitogen-activated protein kinase (p38-MAPK) plays a key role in lipopolysaccharide-induced tumor necrosis factor-α (TNF-α) and interleukin-1 (IL-1) release during the inflammatory process, emerging as an attractive target for new anti-inflammatory agents. Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis [Hopfinger et al., J. Am. Chem. Soc., 119 (1997) 10509] was applied to a series of 33 (a training set of 28 and a test set of 5) pyridinyl-imidazole and pyrimidinyl-imidazole inhibitors of p38-MAPK, with IC50 ranging from 0.11 to 2100 nM [Liverton et al., J. Med. Chem., 42 (1999) 2180]. Five thousand conformations of each analogue were sampled from a molecular dynamics simulation (MDS) during 50 ps at a constant temperature of 303 K. Each conformation was placed in a 2 Å grid cell lattice for each of three trial alignments. 4D-QSAR models were constructed by genetic algorithm (GA) optimization and partial least squares (PLS) fitting, and evaluated by leave-one-out cross-validation technique. In the best models, with three to six terms, the adjusted cross-validated squared correlation coefficients, Q 2 adj, ranged from 0.67 to 0.85. Model D ( Q 2 adj = 0.84) was identified as the most robust model from alignment 1, and it is representative of the other best models. This model encompasses new molecular regions as containing pharmacophore sites, such as the amino-benzyl moiety of pyrimidine analogs and the N1-substituent in the imidazole ring. These regions of the ligands should be further explored to identify better anti-inflammatory inhibitors of p38-MAPK.

  1. Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives.

    PubMed

    Toropova, Alla P; Schultz, Terry W; Toropov, Andrey A

    2016-03-01

    Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. COREPA-M: NEW MULTI-DIMENSIONAL FUNCTIONALITY OF THE COREPA METHOD

    EPA Science Inventory

    The COmmon REactivity PAttern (COREPA) method is a recently developed pattern recognition technique accounting for conformational flexibility of chemicals in 3-D quantitative structure-activity relationships (QSARs). The method is based on the assumption that non-congeneric chemi...

  3. High Add Valued Application of Turpentine in Crop Production through Structural Modification and QSAR Analysis.

    PubMed

    Gao, Yanqing; Li, Jingjing; Li, Jian; Song, Zhanqian; Shang, Shibin; Rao, Xiaoping

    2018-02-08

    Turpentine is a volatile component of resin, which is an abundant forest resource in Southern China. As one of the most important components, the integrated application of β-pinene has been studied. The broad-spectrum evaluation of β-pinene and its analogues has, therefore, been necessary. In an attempt to expand the scope of agro-activity trials, the preparation and the evaluation of the herbicidal activity of a series of β-pinene analogues against three agricultural herbs were carried out. In accordance with the overall herbicidal activity, it is noteworthy that compounds 6k , 6l , and 6m demonstrated extreme activity with IC 50 values of 0.065, 0.065, and 0.052 mol active ingredients/hectare against E. crus-galli . The preliminary structure-activity relationship (SAR) was analyzed and the compounds with the appropriate volatility and substituent type that had beneficial herbicidal activity were analyzed. Simultaneously, the quantitative structure-activity relationship (QSAR) model was built and the most important structural features were indicated, which was, to a certain extent, in line with the SAR study. The study aimed to study the application of the forest resource turpentine in agriculture as a potential and alternative approach for comprehensive utilization.

  4. Antitumor activity of 3,4-ethylenedioxythiophene derivatives and quantitative structure-activity relationship analysis

    NASA Astrophysics Data System (ADS)

    Jukić, Marijana; Rastija, Vesna; Opačak-Bernardi, Teuta; Stolić, Ivana; Krstulović, Luka; Bajić, Miroslav; Glavaš-Obrovac, Ljubica

    2017-04-01

    The aim of this study was to evaluate nine newly synthesized amidine derivatives of 3,4- ethylenedioxythiophene (3,4-EDOT) for their cytotoxic activity against a panel of human cancer cell lines and to perform a quantitative structure-activity relationship (QSAR) analysis for the antitumor activity of a total of 27 3,4-ethylenedioxythiophene derivatives. Induction of apoptosis was investigated on the selected compounds, along with delivery options for the optimization of activity. The best obtained QSAR models include the following group of descriptors: BCUT, WHIM, 2D autocorrelations, 3D-MoRSE, GETAWAY descriptors, 2D frequency fingerprint and information indices. Obtained QSAR models should be relieved in elucidation of important physicochemical and structural requirements for this biological activity. Highly potent molecules have a symmetrical arrangement of substituents along the x axis, high frequency of distance between N and O atoms at topological distance 9, as well as between C and N atoms at topological distance 10, and more C atoms located at topological distances 6 and 3. Based on the conclusion given in the QSAR analysis, a new compound with possible great activity was proposed.

  5. Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.

    PubMed

    Mohr, Johannes A; Jain, Brijnesh J; Obermayer, Klaus

    2008-09-01

    Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.

  6. The influence of data curation on QSAR Modeling - examining issues of quality versus quantity of data (American Chemical Society)

    EPA Science Inventory

    This presentation will examine the impact of data quality on the construction of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. We have developed a public-facing platform to provide access to predictive models. As part of the work we ha...

  7. Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment.

    PubMed Central

    McKim, J M; Bradbury, S P; Niemi, G J

    1987-01-01

    Implementation of the Toxic Substances Control Act of 1977 creates the need to reliably establish testing priorities because laboratory resources are limited and the number of industrial chemicals requiring evaluation is overwhelming. The use of quantitative structure activity relationship (QSAR) models as rapid and predictive screening tools to select more potentially hazardous chemicals for in-depth laboratory evaluation has been proposed. Further implementation and refinement of quantitative structure-toxicity relationships in aquatic toxicology and hazard assessment requires the development of a "mode-of-action" database. With such a database, a qualitative structure-activity relationship can be formulated to assign the proper mode of action, and respective QSAR, to a given chemical structure. In this review, the development of fish acute toxicity syndromes (FATS), which are toxic-response sets based on various behavioral and physiological-biochemical measurements, and their projected use in the mode-of-action database are outlined. Using behavioral parameters monitored in the fathead minnow during acute toxicity testing, FATS associated with acetylcholinesterase (AChE) inhibitors and narcotics could be reliably predicted. However, compounds classified as oxidative phosphorylation uncouplers or stimulants could not be resolved. Refinement of this approach by using respiratory-cardiovascular responses in the rainbow trout, enabled FATS associated with AChE inhibitors, convulsants, narcotics, respiratory blockers, respiratory membrane irritants, and uncouplers to be correctly predicted. PMID:3297660

  8. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.

    PubMed

    Esposito, Emilio Xavier; Hopfinger, Anton J; Shao, Chi-Yu; Su, Bo-Han; Chen, Sing-Zuo; Tseng, Yufeng Jane

    2015-10-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Ester of Quinoxaline-7-carboxylate 1,4-di-N-oxide as Apoptosis Inductors in K-562 Cell Line: An in vitro, QSAR and DFT Study.

    PubMed

    Rivera, Gildardo; Andrade-Ochoa, Sergio; Romero, Manolo S Ortega; Palos, Isidro; Monge, Antonio; Sanchez-Torres, Luvia Enid

    2017-01-01

    Quinoxalines have shown a wide variety of biological activities including as antitumor agents. The aims of this study were to evaluate the activity of quinoxaline 1,4-di-N-oxide derivatives on K562 cells, the establishment of the mechanism of induced cell death, and the construction of predictive QSAR models. Sixteen esters of quinoxaline-7-carboxylate 1,4-di-N-oxide were evaluated for antitumor activity on K562 chronic myelogenous leukemia cells and their IC50 values were determined. The mechanism of induced cell death by the most active molecule was assessed by flow cytometry and an in silico study was conducted to optimize and calculate theoretical descriptors of all quinoxaline 1,4-di-N-oxide derivatives. QSAR and QPAR models were created using genetic algorithms. Our results show that compounds C5, C7, C10, C12 and C15 had the lowest IC50 of the series. C15 was the most active compound (IC50= 3.02 μg/mL), inducing caspase-dependent apoptotic cell death via the intrinsic pathway. QSAR and QPAR studies are discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  10. QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites.

    PubMed

    Correa-Basurto, J; Bello, M; Rosales-Hernández, M C; Hernández-Rodríguez, M; Nicolás-Vázquez, I; Rojo-Domínguez, A; Trujillo-Ferrara, J G; Miranda, René; Flores-Sandoval, C A

    2014-02-25

    A set of 84 known N-aryl-monosubstituted derivatives (42 amides: series 1 and 2, and 42 imides: series 3 an 4, from maleic and succinic anhydrides, respectively) that display inhibitory activity toward both acetylcholinesterase and butyrylcholinesterase (ChEs) was considered for Quantitative structure-activity relationship (QSAR) studies. These QSAR studies employed docking data from both ChEs that were previously submitted to molecular dynamics (MD) simulations. Donepezil and galanthamine stereoisomers were included to analyze their quantum mechanics properties and for validating the docking procedure. Quantum parameters such as frontier orbital energies, dipole moment, molecular volume, atomic charges, bond length and reactivity parameters were measured, as well as partition coefficients, molar refractivity and polarizability were also analyzed. In order to evaluate the obtained equations, four compounds: 1a (4-oxo-4-(phenylamino)butanoic acid), 2a ((2Z)-4-oxo-4-(phenylamino)but-2-enoic acid), 3a (2-phenylcyclopentane-1,3-dione) and 4a (2-phenylcyclopent-4-ene-1,3-dione) were employed as independent data set, using only equations with r(m(test))²>0.5. It was observed that residual values gave low value in almost all series, excepting in series 1 for compounds 3a and 4a, and in series 4 for compounds 1a, 2a and 3a, giving a low value for 4a. Consequently, equations seems to be specific according to the structure of the evaluated compound, that means, series 1 fits better for compound 1a, series 3 or 4 fits better for compounds 3a or 4a. Same behavior was observed in the butyrylcholinesterase (BChE). Therefore, obtained equations in this QSAR study could be employed to calculate the inhibition constant (Ki) value for compounds having a similar structure as N-aryl derivatives described here. The QSAR study showed that bond lengths, molecular electrostatic potential and frontier orbital energies are important in both ChE targets. Docking studies revealed that

  11. Development of an acute oral toxicity dataset to facilitate assessment of existing QSARs and development of new models (WC10)

    EPA Science Inventory

    Acute oral toxicity data are used to meet both regulatory and non-regulatory needs. Recently, there have been efforts to explore alternative approaches for predicting acute oral toxicity such as QSARs. Evaluating the performance and scope of existing models and investigating the ...

  12. Development of quantitative structure-activity relationships and its application in rational drug design.

    PubMed

    Yang, Guang-Fu; Huang, Xiaoqin

    2006-01-01

    Over forty years have elapsed since Hansch and Fujita published their pioneering work of quantitative structure-activity relationships (QSAR). Following the introduction of Comparative Molecular Field Analysis (CoMFA) by Cramer in 1998, other three-dimensional QSAR methods have been developed. Currently, combination of classical QSAR and other computational techniques at three-dimensional level is of greatest interest and generally used in the process of modern drug discovery and design. During the last several decades, a number of different mythologies incorporating a range of molecular descriptors and different statistical regression ways have been proposed and successfully applied in developing of new drugs, thus QSAR method has been proven to be indispensable in not only the reliable prediction of specific properties of new compounds, but also the help to elucidate the possible molecular mechanism of the receptor-ligand interactions. Here, we review the recent developments in QSAR and their applications in rational drug design, focusing on the reasonable selection of novel molecular descriptors and the construction of predictive QSAR models by the help of advanced computational techniques.

  13. DFT and 3D-QSAR Studies of Anti-Cancer Agents m-(4-Morpholinoquinazolin-2-yl) Benzamide Derivatives for Novel Compounds Design

    NASA Astrophysics Data System (ADS)

    Zhao, Siqi; Zhang, Guanglong; Xia, Shuwei; Yu, Liangmin

    2018-06-01

    As a group of diversified frameworks, quinazolin derivatives displayed a broad field of biological functions, especially as anticancer. To investigate the quantitative structure-activity relationship, 3D-QSAR models were generated with 24 quinazolin scaffold molecules. The experimental and predicted pIC50 values for both training and test set compounds showed good correlation, which proved the robustness and reliability of the generated QSAR models. The most effective CoMFA and CoMSIA were obtained with correlation coefficient r 2 ncv of 1.00 (both) and leave-one-out coefficient q 2 of 0.61 and 0.59, respectively. The predictive abilities of CoMFA and CoMSIA were quite good with the predictive correlation coefficients ( r 2 pred ) of 0.97 and 0.91. In addition, the statistic results of CoMFA and CoMSIA were used to design new quinazolin molecules.

  14. QSAR Classification of ToxCast and Tox21 Chemicals on the Basis of Estrogen Receptor Assays (FutureToxII)

    EPA Science Inventory

    The ToxCast and Tox21 programs have tested ~8,200 chemicals in a broad screening panel of in vitro high-throughput screening (HTS) assays for estrogen receptor (ER) agonist and antagonist activity. The present work uses this large in vitro data set to develop in silico QSAR model...

  15. A physically interpretable quantum-theoretic QSAR for some carbonic anhydrase inhibitors with diverse aromatic rings, obtained by a new QSAR procedure.

    PubMed

    Clare, Brian W; Supuran, Claudiu T

    2005-03-15

    A QSAR based almost entirely on quantum theoretically calculated descriptors has been developed for a large and heterogeneous group of aromatic and heteroaromatic carbonic anhydrase inhibitors, using orbital energies, nodal angles, atomic charges, and some other intuitively appealing descriptors. Most calculations have been done at the B3LYP/6-31G* level of theory. For the first time we have treated five-membered rings by the same means that we have used for benzene rings in the past. Our flip regression technique has been expanded to encompass automatic variable selection. The statistical quality of the results, while not equal to those we have had with benzene derivatives, is very good considering the noncongeneric nature of the compounds. The most significant correlation was with charge on the atoms of the sulfonamide group, followed by the nodal orientation and the solvation energy calculated by COSMO and the charge polarization of the molecule calculated as the mean absolute Mulliken charge over all atoms.

  16. 3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

    PubMed

    Ding, Lina; Wang, Zhi-Zheng; Sun, Xu-Dong; Yang, Jing; Ma, Chao-Ya; Li, Wen; Liu, Hong-Min

    2017-08-01

    Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q 2 =0.802, r 2 ncv =0.979, and the best CoMSIA model has q 2 =0.799, r 2 ncv =0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design. Copyright © 2017. Published by Elsevier Ltd.

  17. Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign

    NASA Astrophysics Data System (ADS)

    Sliwoski, Gregory; Mendenhall, Jeffrey; Meiler, Jens

    2016-03-01

    Quantitative structure-activity relationship (QSAR) is a branch of computer aided drug discovery that relates chemical structures to biological activity. Two well established and related QSAR descriptors are two- and three-dimensional autocorrelation (2DA and 3DA). These descriptors encode the relative position of atoms or atom properties by calculating the separation between atom pairs in terms of number of bonds (2DA) or Euclidean distance (3DA). The sums of all values computed for a given small molecule are collected in a histogram. Atom properties can be added with a coefficient that is the product of atom properties for each pair. This procedure can lead to information loss when signed atom properties are considered such as partial charge. For example, the product of two positive charges is indistinguishable from the product of two equivalent negative charges. In this paper, we present variations of 2DA and 3DA called 2DA_Sign and 3DA_Sign that avoid information loss by splitting unique sign pairs into individual histograms. We evaluate these variations with models trained on nine datasets spanning a range of drug target classes. Both 2DA_Sign and 3DA_Sign significantly increase model performance across all datasets when compared with traditional 2DA and 3DA. Lastly, we find that limiting 3DA_Sign to maximum atom pair distances of 6 Å instead of 12 Å further increases model performance, suggesting that conformational flexibility may hinder performance with longer 3DA descriptors. Consistent with this finding, limiting the number of bonds in 2DA_Sign from 11 to 5 fails to improve performance.

  18. QSAR, docking and ADMET studies of artemisinin derivatives for antimalarial activity targeting plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum.

    PubMed

    Qidwai, Tabish; Yadav, Dharmendra K; Khan, Feroz; Dhawan, Sangeeta; Bhakuni, R S

    2012-01-01

    This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.

  19. Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K as anti-malarial agents

    NASA Astrophysics Data System (ADS)

    Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

    2017-06-01

    The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

  20. Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents.

    PubMed

    Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

    2017-01-01

    The Plasmodium falciparum Lactate Dehydrogenase enzyme ( Pf LDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The Pf LDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of Pf LDH, we have used Discovery studio to perform molecular docking in the active binding pocket of Pf LDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q 2 of 0.516. The model has predicted r 2 of 0.91 showing that predicted IC 50 values are in good agreement with experimental IC 50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

  1. Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents

    PubMed Central

    Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

    2017-01-01

    The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors. PMID:28664157

  2. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling.

    PubMed

    Mansouri, K; Grulke, C M; Richard, A M; Judson, R S; Williams, A J

    2016-11-01

    The increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experimental data. Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publicly available PHYSPROP physicochemical properties and environmental fate datasets. The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name, CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only the highest-quality subset of the original dataset was compared with the larger curated and corrected dataset. The latter showed statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and is being made publicly available for further usage and integration by the scientific community.

  3. 2D-QSAR study, molecular docking, and molecular dynamics simulation studies of interaction mechanism between inhibitors and transforming growth factor-beta receptor I (ALK5).

    PubMed

    Jiang, Meng-Nan; Zhou, Xiao-Ping; Sun, Dong-Ru; Gao, Huan; Zheng, Qing-Chuan; Zhang, Hong-Xing; Liang, Di

    2017-11-06

    Transforming growth factor type 1 receptor (ALK5) is kinase associated with a wide variety of pathological processes, and inhibition of ALK5 is a good strategy to treat many kinds of cancer and fibrotic diseases. Recently, a series of compounds have been synthesized as ALK5 inhibitors. However, the study of their selectivity against other potential targets remains elusive. In this research, a data-set of ALK5 inhibitors were collected and studied based on the combination of 2D-QSAR, molecular docking and molecular dynamics simulation. The quality of QSAR models were assessed statistically by F, R 2 , and R 2 ADJ , proved to be credible. The cross-validations for the models (q 2 LOO  = 0.571 and 0.629, respectively) showed their robustness, while the external validations (r 2 test  = 0.703 and 0.764, respectively) showed their predictive power. Besides, the predicted binding free energy results calculated by MM/GBSA method were in accordance with the experimental data, and the van der Waals energy term was the factor that had the most significant impact on ligand binding. What is more, several important residues were found to significantly affect the binding affinity. Finally, based on our analyses above, a proposed series of molecules were designed.

  4. Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

    PubMed

    Barber, Chris; Cayley, Alex; Hanser, Thierry; Harding, Alex; Heghes, Crina; Vessey, Jonathan D; Werner, Stephane; Weiner, Sandy K; Wichard, Joerg; Giddings, Amanda; Glowienke, Susanne; Parenty, Alexis; Brigo, Alessandro; Spirkl, Hans-Peter; Amberg, Alexander; Kemper, Ray; Greene, Nigel

    2016-04-01

    The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. The Role of Feature Selection and Statistical Weighting in Predicting In Vivo Toxicity Using In Vitro Assay and QSAR Data (SOT)

    EPA Science Inventory

    Our study assesses the value of both in vitro assay and quantitative structure activity relationship (QSAR) data in predicting in vivo toxicity using numerous statistical models and approaches to process the data. Our models are built on datasets of (i) 586 chemicals for which bo...

  6. A QSAR-like analysis of the adsorption of endocrine disrupting compounds, pharmaceuticals, and personal care products on modified activated carbons.

    PubMed

    Redding, Adam M; Cannon, Fred S; Snyder, Shane A; Vanderford, Brett J

    2009-08-01

    Rapid small-scale column tests (RSSCTs) examined the removal of 29 endocrine disrupting compounds (EDCs) and pharmaceutical/personal care products (PPCPs). The RSSCTs employed three lignite variants: HYDRODARCO 4000 (HD4000), steam-modified HD4000, and methane/steam-modified HD4000. RSSCTs used native Lake Mead, NV water spiked with 100-200 ppt each of 29 EDCs/PPCPs. For the steam and methane/steam variants, breakthrough occurred at 14,000-92,000 bed volumes (BV); and this was 3-4 times more bed volumes than for HD4000. Most EDC/PPCP bed life data were describable by a normalized quantitative structure-activity relationship (i.e. QSAR-like model) of the form: where TPV is the pore volume, rho(mc) is the apparent density, CV is the molecular volume, C(o) is the concentration, (8)chi(p) depicts the molecule's compactness, and FOSA is the molecule's hydrophobic surface area.

  7. 3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model.

    PubMed

    Ul-Haq, Zaheer; Ashraf, Sajda; Al-Majid, Abdullah Mohammed; Barakat, Assem

    2016-04-30

    Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q²) value of 0.597 and correlation coefficients (r²) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q² and r² of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r²pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity.

  8. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes.

    PubMed

    Kiwamoto, R; Spenkelink, A; Rietjens, I M C M; Punt, A

    2015-01-01

    Acyclic α,β-unsaturated aldehydes present in food raise a concern because the α,β-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,β-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure-activity relationships (QSARs) defined with a training set of six selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development.

    PubMed

    Tetko, Igor V; Maran, Uko; Tropsha, Alexander

    2017-03-01

    Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    PubMed Central

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  11. Combining molecular docking and QSAR studies for modeling the anti-tyrosinase activity of aromatic heterocycle thiosemicarbazone analogues

    NASA Astrophysics Data System (ADS)

    Dong, Huanhuan; Liu, Jing; Liu, Xiaoru; Yu, Yanying; Cao, Shuwen

    2018-01-01

    A collection of thirty-six aromatic heterocycle thiosemicarbazone analogues presented a broad span of anti-tyrosinase activities were designed and obtained. A robust and reliable two-dimensional quantitative structure-activity relationship model, as evidenced by the high q2 and r2 values (0.848 and 0.893, respectively), was gained based on the analogues to predict the quantitative chemical-biological relationship and the new modifier direction. Inhibitory activities of the compounds were found to greatly depend on molecular shape and orbital energy. Substituents brought out large ovality and high highest-occupied molecular orbital energy values helped to improve the activity of these analogues. The molecular docking results provided visual evidence for QSAR analysis and inhibition mechanism. Based on these, two novel tyrosinase inhibitors O04 and O05 with predicted IC50 of 0.5384 and 0.8752 nM were designed and suggested for further research.

  12. Incorporation of absorption and metabolism into liver toxicity prediction for phytochemicals: A tiered in silico QSAR approach.

    PubMed

    Liu, Yitong

    2018-05-18

    An increased use of herbal dietary supplements has been associated with adverse liver effects such as elevated serum enzymes and liver failure. The safety assessment for herbal dietary supplements is challenging since they often contain complex mixtures of phytochemicals, most of which have unknown pharmacokinetic and toxicological properties. Rapid tools are needed to evaluate large numbers of phytochemicals for potential liver toxicity. The current study demonstrates a tiered approach combining identification of phytochemicals in liver toxic botanicals, followed by in silico quantitative structure-activity relationship (QSAR) evaluation of these phytochemicals for absorption (e.g. permeability), metabolism (cytochromes P450) and liver toxicity (e.g. elevated transaminases). First, 255 phytochemicals from 20 botanicals associated with clinical liver injury were identified, and the phytochemical structures were subsequently used for QSAR evaluation. Among these identified phytochemicals, 193 were predicted to be absorbed and then used to generate metabolites, which were both used to predict liver toxicity. Forty-eight phytochemicals were predicted as liver toxic, either due to parent phytochemicals or metabolites. Among them, nineteen phytochemicals have previous evidence of liver toxicity (e.g. pyrrolizidine alkaloids), while the majority were newly discovered (e.g. sesquiterpenoids). These findings help reveal new toxic phytochemicals in herbal dietary supplements and prioritize future toxicological testing. Published by Elsevier Ltd.

  13. Deep Eutectic Solvents as Convenient Media for Synthesis of Novel Coumarinyl Schiff Bases and Their QSAR Studies.

    PubMed

    Molnar, Maja; Komar, Mario; Brahmbhatt, Harshad; Babić, Jurislav; Jokić, Stela; Rastija, Vesna

    2017-09-05

    Deep eutectic solvents, as green and environmentally friendly media, were utilized in the synthesis of novel coumarinyl Schiff bases. Novel derivatives were synthesized from 2-((4-methyl-2-oxo-2 H -chromen-7-yl)oxy)acetohydrazide and corresponding aldehyde in choline chloride:malonic acid (1:1) based deep eutectic solvent. In these reactions, deep eutectic solvent acted as a solvent and catalyst as well. Novel Schiff bases were synthesized in high yields (65-75%) with no need for further purification, and their structures were confirmed by mass spectra, ¹H and 13 C NMR. Furthermore, their antioxidant activity was determined and compared to antioxidant activity of previously synthesized derivatives, thus investigating their structure-activity relationship utilizing quantitative structure-activity relationship QSAR studies. Calculation of molecular descriptors has been performed by DRAGON software. The best QSAR model ( R tr = 0.636; R ext = 0.709) obtained with three descriptors ( MATS3m , Mor22u , Hy ) implies that the pairs of atoms higher mass at the path length 3, three-dimensional arrangement of atoms at scattering parameter s = 21 Å - ¹, and higher number of hydrophilic groups (-OH, -NH) enhanced antioxidant activity. Electrostatic potential surface of the most active compounds showed possible regions for donation of electrons to 1,1-diphenyl-2-picryhydrazyl (DPPH) radicals.

  14. Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

    PubMed

    Singh, Nidhi; Shah, Priyanka; Dwivedi, Hemlata; Mishra, Shikha; Tripathi, Renu; Sahasrabuddhe, Amogh A; Siddiqi, Mohammad Imran

    2016-11-15

    N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, a TbNMT complex model was generated to establish the relationship between the binding mode of the inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies. A 3D-QSAR model was developed and used to predict the activity of the proposed hits in the subsequent step. The hits classified as active based on the machine learning model were assessed as the potential anti-trypanosomal NMT inhibitors through molecular docking studies, predicted activity using a QSAR model and visual inspection. In the final step, the proposed pipeline was validated through in vitro experiments. A total of seven hits have been proposed and tested in vitro for evaluation of dual inhibitory activity against Leishmania donovani and Trypanosoma brucei. Out of these five compounds showed significant inhibition against both of the organisms. The common topmost active compound SEW04173 belongs to a pyrazole carboxylate scaffold and is anticipated to enrich the chemical space with enhanced potency through optimization.

  15. QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein.

    PubMed

    González-Díaz, Humberto; Dea-Ayuela, María A; Pérez-Montoto, Lázaro G; Prado-Prado, Francisco J; Agüero-Chapín, Guillermín; Bolas-Fernández, Francisco; Vazquez-Padrón, Roberto I; Ubeira, Florencio M

    2010-05-01

    The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

  16. From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors.

    PubMed

    Eren, Gokcen; Macchiarulo, Antonio; Banoglu, Erden

    2012-02-01

    Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors.

    PubMed

    Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

    2016-10-01

    Janus kinase 1 (JAK 1) plays a critical role in initiating responses to cytokines by the JAK-signal transducer and activator of transcription (JAK-STAT). This controls survival, proliferation and differentiation of a variety of cells. Docking, 3D quantitative structure activity relationship (3D-QSAR) and molecular dynamics (MD) studies were performed on a series of Imidazo-pyrrolopyridine derivatives reported as JAK 1 inhibitors. QSAR model was generated using 30 molecules in the training set; developed model showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of this model was determined using a test set of 13 molecules that gave acceptable predictive correlation (r 2 Pred ) values. Finally, molecular dynamics simulation was performed to validate docking results and MM/GBSA calculations. This facilitated us to compare binding free energies of cocrystal ligand and newly designed molecule R1. The good concordance between the docking results and CoMFA/CoMSIA contour maps afforded obliging clues for the rational modification of molecules to design more potent JAK 1 inhibitors. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh

    2013-10-01

    Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα bindingmore » affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and

  19. QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

    PubMed Central

    Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

    2011-01-01

    Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

  20. Application of two-dimensional binary fingerprinting methods for the design of selective Tankyrase I inhibitors.

    PubMed

    Muddukrishna, B S; Pai, Vasudev; Lobo, Richard; Pai, Aravinda

    2017-11-22

    In the present study, five important binary fingerprinting techniques were used to model novel flavones for the selective inhibition of Tankyrase I. From the fingerprints used: the fingerprint atom pairs resulted in a statistically significant 2D QSAR model using a kernel-based partial least square regression method. This model indicates that the presence of electron-donating groups positively contributes to activity, whereas the presence of electron withdrawing groups negatively contributes to activity. This model could be used to develop more potent as well as selective analogues for the inhibition of Tankyrase I. Schematic representation of 2D QSAR work flow.

  1. Uptake and localization mechanisms of fluorescent and colored lipid probes. Part 2. QSAR models that predict localization of fluorescent probes used to identify ("specifically stain") various biomembranes and membranous organelles.

    PubMed

    Horobin, R W; Stockert, J C; Rashid-Doubell, F

    2015-05-01

    We discuss a variety of biological targets including generic biomembranes and the membranes of the endoplasmic reticulum, endosomes/lysosomes, Golgi body, mitochondria (outer and inner membranes) and the plasma membrane of usual fluidity. For each target, we discuss the access of probes to the target membrane, probe uptake into the membrane and the mechanism of selectivity of the probe uptake. A statement of the QSAR decision rule that describes the required physicochemical features of probes that enable selective staining also is provided, followed by comments on exceptions and limits. Examples of probes typically used to demonstrate each target structure are noted and decision rule tabulations are provided for probes that localize in particular targets; these tabulations show distribution of probes in the conceptual space defined by the relevant structure parameters ("parameter space"). Some general implications and limitations of the QSAR models for probe targeting are discussed including the roles of certain cell and protocol factors that play significant roles in lipid staining. A case example illustrates the predictive ability of QSAR models. Key limiting values of the head group hydrophilicity parameter associated with membrane-probe interactions are discussed in an appendix.

  2. Estimating the fates of organic contaminants in an aquifer using QSAR.

    PubMed

    Lim, Seung Joo; Fox, Peter

    2013-01-01

    The quantitative structure activity relationship (QSAR) model, BIOWIN, was modified to more accurately estimate the fates of organic contaminants in an aquifer. The predictions from BIOWIN were modified to include oxidation and sorption effects. The predictive model therefore included the effects of sorption, biodegradation, and oxidation. A total of 35 organic compounds were used to validate the predictive model. The majority of the ratios of predicted half-life to measured half-life were within a factor of 2 and no ratio values were greater than a factor of 5. In addition, the accuracy of estimating the persistence of organic compounds in the sub-surface was superior when modified by the relative fraction adsorbed to the solid phase, 1/Rf, to that when modified by the remaining fraction of a given compound adsorbed to a solid, 1 - fs.

  3. QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities.

    PubMed

    Lima, Marilia N N; Melo-Filho, Cleber C; Cassiano, Gustavo C; Neves, Bruno J; Alves, Vinicius M; Braga, Rodolpho C; Cravo, Pedro V L; Muratov, Eugene N; Calit, Juliana; Bargieri, Daniel Y; Costa, Fabio T M; Andrade, Carolina H

    2018-01-01

    Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium , affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum ( Pf dUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei . We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as Pf dUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual Pf dUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.

  4. Docking and 3-D QSAR studies on indolyl aryl sulfones. Binding mode exploration at the HIV-1 reverse transcriptase non-nucleoside binding site and design of highly active N-(2-hydroxyethyl)carboxamide and N-(2-hydroxyethyl)carbohydrazide derivatives.

    PubMed

    Ragno, Rino; Artico, Marino; De Martino, Gabriella; La Regina, Giuseppe; Coluccia, Antonio; Di Pasquali, Alessandra; Silvestri, Romano

    2005-01-13

    Three-dimensional quantitative structure-activity relationship (3-D QSAR) studies and docking simulations were developed on indolyl aryl sulfones (IASs), a class of novel HIV-1 non-nucleoside reverse transcriptase (RT) inhibitors (Silvestri, et al. J. Med. Chem. 2003, 46, 2482-2493) highly active against wild type and some clinically relevant resistant strains (Y181C, the double mutant K103N-Y181C, and the K103R-V179D-P225H strain, highly resistant to efavirenz). Predictive 3-D QSAR models using the combination of GRID and GOLPE programs were obtained using a receptor-based alignment by means of docking IASs into the non-nucleoside binding site (NNBS) of RT. The derived 3-D QSAR models showed conventional correlation (r(2)) and cross-validated (q(2)) coefficients values ranging from 0.79 to 0.93 and from 0.59 to 0.84, respectively. All described models were validated by an external test set compiled from previously reported pyrryl aryl sulfones (Artico, et al. J. Med. Chem. 1996, 39, 522-530). The most predictive 3-D QSAR model was then used to predict the activity of novel untested IASs. The synthesis of six designed derivatives (prediction set) allowed disclosure of new IASs endowed with high anti-HIV-1 activities.

  5. Environmental risk assessment of selected organic chemicals based on TOC test and QSAR estimation models.

    PubMed

    Chi, Yulang; Zhang, Huanteng; Huang, Qiansheng; Lin, Yi; Ye, Guozhu; Zhu, Huimin; Dong, Sijun

    2018-02-01

    Environmental risks of organic chemicals have been greatly determined by their persistence, bioaccumulation, and toxicity (PBT) and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor (BCF) and octanol-water partition coefficient (Kow), which frequently displays a substantial correlation with the sediment sorption coefficient (Koc). Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship (QSAR) estimation models are indispensable for predicting environmental fate and health effects in the absence of field- or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon (TOC) degradation, and two widely accepted and highly used QSAR estimation models (i.e., EPI Suite and PBT Profiler) were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment. However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants. Copyright © 2016. Published by Elsevier B.V.

  6. Atom-based 3D-QSAR, induced fit docking, and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5.

    PubMed

    Vijaya Prabhu, Sitrarasu; Singh, Sanjeev Kumar

    2018-05-28

    Atom-based three dimensional-quantitative structure-activity relationship (3D-QSAR) model was developed on the basis of 5-point pharmacophore hypothesis (AARRR) with two hydrogen bond acceptors (A) and three aromatic rings for the derivatives of thieno[2,3-b]pyridine, which modulates the activity to inhibit the mGluR5 receptor. Generation of a highly predictive 3D-QSAR model was performed using the alignment of predicted pharmacophore hypothesis for the training set (R 2  = 0.84, SD = 0.26, F = 45.8, N = 29) and test set (Q 2  = 0.74, RMSE = 0.235, Pearson-R = 0.94, N = 9). The best pharmacophore hypothesis AARRR was selected, and developed three dimensional-quantitative structure activity relationship (3D-QSAR) model also supported the outcome of this study by means of favorable and unfavorable electron withdrawing group and hydrophobic regions of most active compound 42d and least active compound 18b. Following, induced fit docking and binding free energy calculations reveals the reliable binding orientation of the compounds. Finally, molecular dynamics simulations for 100 ns were performed to depict the protein-ligand stability. We anticipate that the resulted outcome could be supportive to discover potent negative allosteric modulators for metabotropic glutamate receptor 5 (mGluR5).

  7. QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1.

    PubMed

    Comelli, Nieves C; Duchowicz, Pablo R; Castro, Eduardo A

    2014-10-01

    The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Three-dimensional quantitative structure-activity relationship (3D QSAR) and pharmacophore elucidation of tetrahydropyran derivatives as serotonin and norepinephrine transporter inhibitors

    NASA Astrophysics Data System (ADS)

    Kharkar, Prashant S.; Reith, Maarten E. A.; Dutta, Aloke K.

    2008-01-01

    Three-dimensional quantitative structure-activity relationship (3D QSAR) using comparative molecular field analysis (CoMFA) was performed on a series of substituted tetrahydropyran (THP) derivatives possessing serotonin (SERT) and norepinephrine (NET) transporter inhibitory activities. The study aimed to rationalize the potency of these inhibitors for SERT and NET as well as the observed selectivity differences for NET over SERT. The dataset consisted of 29 molecules, of which 23 molecules were used as the training set for deriving CoMFA models for SERT and NET uptake inhibitory activities. Superimpositions were performed using atom-based fitting and 3-point pharmacophore-based alignment. Two charge calculation methods, Gasteiger-Hückel and semiempirical PM3, were tried. Both alignment methods were analyzed in terms of their predictive abilities and produced comparable results with high internal and external predictivities. The models obtained using the 3-point pharmacophore-based alignment outperformed the models with atom-based fitting in terms of relevant statistics and interpretability of the generated contour maps. Steric fields dominated electrostatic fields in terms of contribution. The selectivity analysis (NET over SERT), though yielded models with good internal predictivity, showed very poor external test set predictions. The analysis was repeated with 24 molecules after systematically excluding so-called outliers (5 out of 29) from the model derivation process. The resulting CoMFA model using the atom-based fitting exhibited good statistics and was able to explain most of the selectivity (NET over SERT)-discriminating factors. The presence of -OH substituent on the THP ring was found to be one of the most important factors governing the NET selectivity over SERT. Thus, a 4-point NET-selective pharmacophore, after introducing this newly found H-bond donor/acceptor feature in addition to the initial 3-point pharmacophore, was proposed.

  9. PBT assessment under REACH: Screening for low aquatic bioaccumulation with QSAR classifications based on physicochemical properties to replace BCF in vivo testing on fish.

    PubMed

    Nendza, Monika; Kühne, Ralph; Lombardo, Anna; Strempel, Sebastian; Schüürmann, Gerrit

    2018-03-01

    Aquatic bioconcentration factors (BCFs) are critical in PBT (persistent, bioaccumulative, toxic) and risk assessment of chemicals. High costs and use of more than 100 fish per standard BCF study (OECD 305) call for alternative methods to replace as much in vivo testing as possible. The BCF waiving scheme is a screening tool combining QSAR classifications based on physicochemical properties related to the distribution (hydrophobicity, ionisation), persistence (biodegradability, hydrolysis), solubility and volatility (Henry's law constant) of substances in water bodies and aquatic biota to predict substances with low aquatic bioaccumulation (nonB, BCF<2000). The BCF waiving scheme was developed with a dataset of reliable BCFs for 998 compounds and externally validated with another 181 substances. It performs with 100% sensitivity (no false negatives), >50% efficacy (waiving potential), and complies with the OECD principles for valid QSARs. The chemical applicability domain of the BCF waiving scheme is given by the structures of the training set, with some compound classes explicitly excluded like organometallics, poly- and perfluorinated compounds, aromatic triphenylphosphates, surfactants. The prediction confidence of the BCF waiving scheme is based on applicability domain compliance, consensus modelling, and the structural similarity with known nonB and B/vB substances. Compounds classified as nonB by the BCF waiving scheme are candidates for waiving of BCF in vivo testing on fish due to low concern with regard to the B criterion. The BCF waiving scheme supports the 3Rs with a possible reduction of >50% of BCF in vivo testing on fish. If the target chemical is outside the applicability domain of the BCF waiving scheme or not classified as nonB, further assessments with in silico, in vitro or in vivo methods are necessary to either confirm or reject bioaccumulative behaviour. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. 7-MEOTA-donepezil like compounds as cholinesterase inhibitors: Synthesis, pharmacological evaluation, molecular modeling and QSAR studies.

    PubMed

    Korabecny, Jan; Dolezal, Rafael; Cabelova, Pavla; Horova, Anna; Hruba, Eva; Ricny, Jan; Sedlacek, Lukas; Nepovimova, Eugenie; Spilovska, Katarina; Andrs, Martin; Musilek, Kamil; Opletalova, Veronika; Sepsova, Vendula; Ripova, Daniela; Kuca, Kamil

    2014-07-23

    A novel series of 7-methoxytacrine (7-MEOTA)-donepezil like compounds was synthesized and tested for their ability to inhibit electric eel acetylcholinesterase (EeAChE), human recombinant AChE (hAChE), equine serum butyrylcholinesterase (eqBChE) and human plasmatic BChE (hBChE). New hybrids consist of a 7-MEOTA unit, representing less toxic tacrine (THA) derivative, connected with analogues of N-benzylpiperazine moieties mimicking N-benzylpiperidine fragment from donepezil. 7-MEOTA-donepezil like compounds exerted mostly non-selective profile in inhibiting cholinesterases of different origin with IC50 ranging from micromolar to sub-micromolar concentration scale. Kinetic analysis confirmed mixed-type inhibition presuming that these inhibitors are capable to simultaneously bind peripheral anionic site (PAS) as well as catalytic anionic site (CAS) of AChE. Molecular modeling studies and QSAR studies were performed to rationalize studies from in vitro. Overall, 7-MEOTA-donepezil like derivatives can be considered as interesting candidates for Alzheimer's disease treatment. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  11. Importance of Kier-Hall topological indices in the QSAR of anticancer drug design.

    PubMed

    Nandi, Sisir; Bagchi, Manish C

    2012-06-01

    An important area of theoretical drug design research is quantitative structure activity relationship (QSAR) using structural invariants. The impetus for this research trend comes from various directions. Researchers in chemical documentation have searched for a set of invariants which will be more convenient than the adjacency matrix (or connection table) for the storage and comparison of chemical structures. Molecular structure can be looked upon as the representation of the relationship among its various constituents. The term molecular structure represents a set of nonequivalent and probably disjoint concepts. There is no reason to believe that when we discuss diverse topics (e.g. chemical synthesis, reaction rates, spectroscopic transitions, reaction mechanisms, and ab initio calculations) using the notion of molecular structure, the different meanings we attach to the single term molecular structure originate from the same fundamental concept. On the contrary, there is a theoretical and philosophical basis for the non-homogeneity of concepts covered by the term molecular structure. In the context of molecular science, the various concepts of molecular structure (e.g. classical valence bond representations, various chemical graph-theoretic representations, ball and spoke model of a molecule, representation of a molecule by minimum energy conformation, semi symbolic contour map of a molecule, or symbolic representation of chemical species by Hamiltonian operators) are model objects derived through different abstractions of the same chemical reality. In each instance, the equivalence class (concept or model of molecular structure) is generated by selecting certain aspects while ignoring some unique properties of those actual events. This explains the plurality of the concept of molecular structure and their autonomous nature, the word autonomous being used in the same sense that one concept is not logically derived from the other. At the most fundamental level

  12. Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

    NASA Astrophysics Data System (ADS)

    Ragno, Rino; Ballante, Flavio; Pirolli, Adele; Wickersham, Richard B.; Patsilinakos, Alexandros; Hesse, Stéphanie; Perspicace, Enrico; Kirsch, Gilbert

    2015-08-01

    Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

  13. 3D-QSAR based on quantum-chemical molecular fields: toward an improved description of halogen interactions.

    PubMed

    Güssregen, Stefan; Matter, Hans; Hessler, Gerhard; Müller, Marco; Schmidt, Friedemann; Clark, Timothy

    2012-09-24

    Current 3D-QSAR methods such as CoMFA or CoMSIA make use of classical force-field approaches for calculating molecular fields. Thus, they can not adequately account for noncovalent interactions involving halogen atoms like halogen bonds or halogen-π interactions. These deficiencies in the underlying force fields result from the lack of treatment of the anisotropy of the electron density distribution of those atoms, known as the "σ-hole", although recent developments have begun to take specific interactions such as halogen bonding into account. We have now replaced classical force field derived molecular fields by local properties such as the local ionization energy, local electron affinity, or local polarizability, calculated using quantum-mechanical (QM) techniques that do not suffer from the above limitation for 3D-QSAR. We first investigate the characteristics of QM-based local property fields to show that they are suitable for statistical analyses after suitable pretreatment. We then analyze these property fields with partial least-squares (PLS) regression to predict biological affinities of two data sets comprising factor Xa and GABA-A/benzodiazepine receptor ligands. While the resulting models perform equally well or even slightly better in terms of consistency and predictivity than the classical CoMFA fields, the most important aspect of these augmented field-types is that the chemical interpretation of resulting QM-based property field models reveals unique SAR trends driven by electrostatic and polarizability effects, which cannot be extracted directly from CoMFA electrostatic maps. Within the factor Xa set, the interaction of chlorine and bromine atoms with a tyrosine side chain in the protease S1 pocket are correctly predicted. Within the GABA-A/benzodiazepine ligand data set, PLS models of high predictivity resulted for our QM-based property fields, providing novel insights into key features of the SAR for two receptor subtypes and cross

  14. 5D-QSAR for spirocyclic sigma1 receptor ligands by Quasar receptor surface modeling.

    PubMed

    Oberdorf, Christoph; Schmidt, Thomas J; Wünsch, Bernhard

    2010-07-01

    Based on a contiguous and structurally as well as biologically diverse set of 87 sigma(1) ligands, a 5D-QSAR study was conducted in which a quasi-atomistic receptor surface modeling approach (program package Quasar) was applied. The superposition of the ligands was performed with the tool Pharmacophore Elucidation (MOE-package), which takes all conformations of the ligands into account. This procedure led to four pharmacophoric structural elements with aromatic, hydrophobic, cationic and H-bond acceptor properties. Using the aligned structures a 3D-model of the ligand binding site of the sigma(1) receptor was obtained, whose general features are in good agreement with previous assumptions on the receptor structure, but revealed some novel insights since it represents the receptor surface in more detail. Thus, e.g., our model indicates the presence of an H-bond acceptor moiety in the binding site as counterpart to the ligands' cationic ammonium center, rather than a negatively charged carboxylate group. The presented QSAR model is statistically valid and represents the biological data of all tested compounds, including a test set of 21 ligands not used in the modeling process, with very good to excellent accuracy [q(2) (training set, n=66; leave 1/3 out) = 0.84, p(2) (test set, n=21)=0.64]. Moreover, the binding affinities of 13 further spirocyclic sigma(1) ligands were predicted with reasonable accuracy (mean deviation in pK(i) approximately 0.8). Thus, in addition to novel insights into the requirements for binding of spirocyclic piperidines to the sigma(1) receptor, the presented model can be used successfully in the rational design of new sigma(1) ligands. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  15. Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model.

    PubMed

    Ghanem, Ouahid Ben; Shah, Syed Nasir; Lévêque, Jean-Marc; Mutalib, M I Abdul; El-Harbawi, Mohanad; Khan, Amir Sada; Alnarabiji, Mohamad Sahban; Al-Absi, Hamada R H; Ullah, Zahoor

    2018-03-01

    Over the past decades, Ionic liquids (ILs) have gained considerable attention from the scientific community in reason of their versatility and performance in many fields. However, they nowadays remain mainly for laboratory scale use. The main barrier hampering their use in a larger scale is their questionable ecological toxicity. This study investigated the effect of hydrophobic and hydrophilic cyclic cation-based ILs against four pathogenic bacteria that infect humans. For that, cations, either of aromatic character (imidazolium or pyridinium) or of non-aromatic nature, (pyrrolidinium or piperidinium), were selected with different alkyl chain lengths and combined with both hydrophilic and hydrophobic anionic moieties. The results clearly demonstrated that introducing of hydrophobic anion namely bis((trifluoromethyl)sulfonyl)amide, [NTF 2 ] and the elongation of the cations substitutions dramatically affect ILs toxicity behaviour. The established toxicity data [50% effective concentration (EC 50 )] along with similar endpoint collected from previous work against Aeromonas hydrophila were combined to developed quantitative structure-activity relationship (QSAR) model for toxicity prediction. The model was developed and validated in the light of Organization for Economic Co-operation and Development (OECD) guidelines strategy, producing good correlation coefficient R 2 of 0.904 and small mean square error (MSE) of 0.095. The reliability of the QSAR model was further determined using k-fold cross validation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Towards the chemometric dissection of peptide - HLA-A*0201 binding affinity: comparison of local and global QSAR models

    NASA Astrophysics Data System (ADS)

    Doytchinova, Irini A.; Walshe, Valerie; Borrow, Persephone; Flower, Darren R.

    2005-03-01

    The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity χ indices, κ shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

  17. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    NASA Astrophysics Data System (ADS)

    Briard, Jennie G.; Fernandez, Michael; de Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-05-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

  18. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    PubMed Central

    Briard, Jennie G.; Fernandez, Michael; De Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-01-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

  19. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

    2016-05-24

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

  20. Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations.

    PubMed

    Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

    2018-02-01

    Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r 2 Pred ) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.

  1. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

    PubMed

    Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

    2006-11-30

    Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

  2. QSAR study on the removal efficiency of organic pollutants in supercritical water based on degradation temperature.

    PubMed

    Jiang, Ai; Cheng, Zhiwen; Shen, Zhemin; Guo, Weimin

    2018-02-13

    This paper aims to study temperature-dependent quantitative structure activity relationship (QSAR) models of supercritical water oxidation (SCWO) process which were developed based on Arrhenius equation between oxidation reaction rate and temperature. Through exploring SCWO process, each kinetic rate constant was studied for 21 organic substances, including azo dyes, heterocyclic compounds and ionic compounds. We propose the concept of T R95 , which is defined as the temperature at removal ratio of 95%, it is a key indicator to evaluate compounds' complete oxidation. By using Gaussian 09 and Material Studio 7.0, quantum chemical parameters were conducted for each organic compound. The optimum model is T R95  = 654.775 + 1761.910f(+) n  - 177.211qH with squared regression coefficient R 2  = 0.620 and standard error SE = 35.1. Nearly all the compounds could obtain accurate predictions of their degradation rate. Effective QSAR model exactly reveals three determinant factors, which are directly related to degradation rules. Specifically, the lowest f(+) value of main-chain atoms (f(+) n ) indicates the degree of affinity for nucleophilic attack. qH shows the ease or complexity of valence-bond breakage of organic molecules. BO x refers to the stability of a bond. Coincidentally, the degradation mechanism could reasonably be illustrated from each perspective, providing a deeper insight of universal and propagable oxidation rules. Besides, the satisfactory results of internal and external validations suggest the stability, reliability and predictive ability of optimum model.

  3. Biological evaluation and 3D-QSAR studies of curcumin analogues as aldehyde dehydrogenase 1 inhibitors.

    PubMed

    Wang, Hui; Du, Zhiyun; Zhang, Changyuan; Tang, Zhikai; He, Yan; Zhang, Qiuyan; Zhao, Jun; Zheng, Xi

    2014-05-16

    Aldehyde dehydrogenase 1 (ALDH1) is reported as a biomarker for identifying some cancer stem cells, and down-regulation or inhibition of the enzyme can be effective in anti-drug resistance and a potent therapeutic for some tumours. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR (three-dimensional quantitative structure activity relationship) of curcumin analogues (CAs) against ALDH1 were studied. Results demonstrated that curcumin and CAs possessed potent inhibitory activity against ALDH1, and the CAs compound with ortho di-hydroxyl groups showed the most potent inhibitory activity. This study indicates that CAs may represent a new class of ALDH1 inhibitor.

  4. In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations.

    PubMed

    Gao, Xiaodong; Han, Liping; Ren, Yujie

    2016-05-05

    Checkpoint kinase 1 (Chk1) is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q² values (0.531, 0.726), fitted correlation r² coefficients (higher than 0.90), and standard error of prediction (less than 0.250). These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.

  5. Studies of tricyclic piperazine/piperidine furnished molecules as novel integrin αvβ3/αIIbβ3 dual antagonists using 3D-QSAR and molecular docking.

    PubMed

    Yan, Yulian; Li, Yan; Zhang, Shuwei; Ai, Chunzhi

    2011-02-01

    The development of injectable integrin α(v)β(3)/α(IIb)β(3) dual antagonists attracts much attention of research for treating of acute ischemic diseases in recent years. In this work, based on a dataset composed of 102 tricyclic piperazine/piperidine furnished dual α(v)β(3) and α(IIb)β(3) antagonists, a variety of in silico modeling approaches including the comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), and molecular docking were applied to reveal the requisite 3D structural features impacting the biological activities. Our statistical results show that the ligand-based 3D-QSAR models for both the α(v)β(3) and α(IIb)β(3) studies exhibited satisfactory internal and external predictability, i.e., for the CoMFA models, results of Q(2)=0.48, R(ncv)(2)=0.87, R(pred)(2)=0.71 for α(v)β(3) and Q(2)=0.50, R(ncv)(2)=0.85, R(pred)(2)=0.72 for α(IIb)β(3) analysis were obtained, and for the CoMSIA ones, the outcomes of Q(2)=0.55, R(ncv)(2)=0.90, R(pred)(2)=0.72 for α(v)β(3) and Q(2)=0.52, R(ncv)(2)=0.88, R(pred)(2)=0.74 for α(IIb)β(3) were achieved respectively. In addition, through a comparison between 3D-QSAR contour maps and docking results, it is revealed that that the most crucial interactions occurring between the tricyclic piperazine/piperidine derivatives and α(v)β(3)/α(IIb)β(3) receptor ligand binding pocket are H-bonding, and the key amino acids impacting the interactions are Arg214, Asn215, Ser123, and Lys253 for α(v)β(3), but Arg214, Asn215, Ser123 and Tyr190 for α(IIb)β(3) receptors, respectively. Halogen-containing groups at position 15 and 16, benzene sulfonamide substituent at position 23, and the replacement of piperazine with 4-aminopiperidine of ring B may increase the α(v)β(3)/α(IIb)β(3) antagonistic activity. The potencies for antagonists to inhibit isolated α(v)β(3) and α(IIb)β(3) are linear correlated, indicating that similar interaction mechanisms may exist for the series

  6. Towards discovering dual functional inhibitors against both wild type and K103N mutant HIV-1 reverse transcriptases: molecular docking and QSAR studies on 4,1-benzoxazepinone analogues

    NASA Astrophysics Data System (ADS)

    Zhang, Zhenshan; Zheng, Mingyue; Du, Li; Shen, Jianhua; Luo, Xiaomin; Zhu, Weiliang; Jiang, Hualiang

    2006-05-01

    To find useful information for discovering dual functional inhibitors against both wild type (WT) and K103N mutant reverse transcriptases (RTs) of HIV-1, molecular docking and 3D-QSAR approaches were applied to a set of twenty-five 4,1-benzoxazepinone analogues of efavirenz (SUSTIVA®), some of them are active against the two RTs. 3D-QSAR models were constructed, based on their binding conformations determined by molecular docking, with r 2 cv values ranging from 0.656 to 0.834 for CoMFA and CoMSIA, respectively. The models were then validated to be highly predictive and extrapolative by inhibitors in two test sets with different molecular skeletons. Furthermore, CoMFA models were found to be well matched with the binding sites of both WT and K103N RTs. Finally, a reasonable pharmacophore model of 4,1-benzoxazepinones were established. The application of the model not only successfully differentiated the experimentally determined inhibitors from non-inhibitors, but also discovered two potent inhibitors from the compound database SPECS. On the basis of both the 3D-QSAR and pharmacophore models, new clues for discovering and designing potent dual functional drug leads against HIV-1 were proposed: (i) adopting positively charged aliphatic group at the cis-substituent of C3; (ii) reducing the electronic density at the position of O4; (iii) positioning a small branched aliphatic group at position of C5; (iv) using the negatively charged bulky substituents at position of C7.

  7. Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb.) K. Schum: Isolation, Synthesis and QSAR Study

    PubMed Central

    Abdullah, Nor Hayati; Thomas, Noel Francis; Sivasothy, Yasodha; Lee, Vannajan Sanghiran; Liew, Sook Yee; Noorbatcha, Ibrahim Ali; Awang, Khalijah

    2016-01-01

    The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R2 = 0.9717, R2cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase. PMID:26907251

  8. 3D-QSAR, molecular dynamics simulations and molecular docking studies of benzoxazepine moiety as mTOR inhibitor for the treatment of lung cancer.

    PubMed

    Chaube, Udit; Chhatbar, Dhara; Bhatt, Hardik

    2016-02-01

    According to WHO statistics, lung cancer is one of the leading causes of death among all other types of cancer. Many genes get mutated in lung cancer but involvement of EGFR and KRAS are more common. Unavailability of drugs or resistance to the available drugs is the major problem in the treatment of lung cancer. In the present research, mTOR was selected as an alternative target for the treatment of lung cancer which involves PI3K/AKT/mTOR pathway. 28 synthetic mTOR inhibitors were selected from the literature. Ligand based approach (CoMFA and CoMSIA) and structure based approach (molecular dynamics simulations assisted molecular docking study) were applied for the identification of important features of benzoxazepine moiety, responsible for mTOR inhibition. Three different alignments were tried to obtain best QSAR model, of which, distil was found to be the best method, as it gave good statistical results. In CoMFA, Leave One Out (LOO) cross validated coefficients (q(2)), conventional coefficient (r(2)) and predicted correlation coefficient (r(2)pred) values were found to be 0.615, 0.990 and 0.930, respectively. Similarly in CoMSIA, q(2), r(2)ncv and r(2)pred values were found to be 0.748, 0.986 and 0.933, respectively. Molecular dynamics and simulations study revealed that B-chain of mTOR protein was stable at and above 500 FS with respect to temperature (at and above 298 K), Potential energy (at and above 7669.72 kJ/mol) and kinetic energy (at and above 4009.77 kJ/mol). Molecular docking study was performed on simulated protein of mTOR which helped to correlate interactions of amino acids surrounded to the ligand with contour maps generated by QSAR method. Important features of benzoxazepine were identified by contour maps and molecular docking study which would be useful to design novel molecules as mTOR inhibitors for the treatment of lung cancer. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. QSAR modeling of acute toxicity on mammals caused by aromatic compounds: the case study using oral LD50 for rats.

    PubMed

    Rasulev, Bakhtiyor; Kusić, Hrvoje; Leszczynska, Danuta; Leszczynski, Jerzy; Koprivanac, Natalija

    2010-05-01

    The goal of the study was to predict toxicity in vivo caused by aromatic compounds structured with a single benzene ring and the presence or absence of different substituent groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc., by using QSAR/QSPR tools. A Genetic Algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. The most predictive model is shown to be the 3-variable model which also has a good ratio of the number of descriptors and their predictive ability to avoid overfitting. The main contributions to the toxicity were shown to be the polarizability weighted MATS2p and the number of certain groups C-026 descriptors. The GA-MLRA approach showed good results in this study, which allows the building of a simple, interpretable and transparent model that can be used for future studies of predicting toxicity of organic compounds to mammals.

  10. Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

    PubMed

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-08-15

    A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.

    PubMed

    Khashan, Raed; Zheng, Weifan; Tropsha, Alexander

    2014-03-01

    We present a novel approach to generating fragment-based molecular descriptors. The molecules are represented by labeled undirected chemical graph. Fast Frequent Subgraph Mining (FFSM) is used to find chemical-fragments (subgraphs) that occur in at least a subset of all molecules in a dataset. The collection of frequent subgraphs (FSG) forms a dataset-specific descriptors whose values for each molecule are defined by the number of times each frequent fragment occurs in this molecule. We have employed the FSG descriptors to develop variable selection k Nearest Neighbor (kNN) QSAR models of several datasets with binary target property including Maximum Recommended Therapeutic Dose (MRTD), Salmonella Mutagenicity (Ames Genotoxicity), and P-Glycoprotein (PGP) data. Each dataset was divided into training, test, and validation sets to establish the statistical figures of merit reflecting the model validated predictive power. The classification accuracies of models for both training and test sets for all datasets exceeded 75 %, and the accuracy for the external validation sets exceeded 72 %. The model accuracies were comparable or better than those reported earlier in the literature for the same datasets. Furthermore, the use of fragment-based descriptors affords mechanistic interpretation of validated QSAR models in terms of essential chemical fragments responsible for the compounds' target property. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

    PubMed

    Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

    2016-05-01

    Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

  13. Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy calculation studies.

    PubMed

    Zhang, Wen; Qiu, Kai-Xiong; Yu, Fang; Xie, Xiao-Guang; Zhang, Shu-Qun; Chen, Ya-Juan; Xie, Hui-Ding

    2017-10-01

    B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG bind ) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC 50 <50μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs. Copyright © 2017. Published by Elsevier Ltd.

  14. Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin

    PubMed Central

    2014-01-01

    Background A number of microtubule disassembly blocking agents and inhibitors of tubulin polymerization have been elements of great interest in anti-cancer therapy, some of them even entering into the clinical trials. One such class of tubulin assembly inhibitors is of arylthioindole derivatives which results in effective microtubule disorganization responsible for cell apoptosis by interacting with the colchicine binding site of the β-unit of tubulin close to the interface with the α unit. We modelled the human tubulin β unit (chain D) protein and performed docking studies to elucidate the detailed binding mode of actions associated with their inhibition. The activity enhancing structural aspects were evaluated using a fragment-based Group QSAR (G-QSAR) model and was validated statistically to determine its robustness. A combinatorial library was generated keeping the arylthioindole moiety as the template and their activities were predicted. Results The G-QSAR model obtained was statistically significant with r2 value of 0.85, cross validated correlation coefficient q2 value of 0.71 and pred_r2 (r2 value for test set) value of 0.89. A high F test value of 65.76 suggests robustness of the model. Screening of the combinatorial library on the basis of predicted activity values yielded two compounds HPI (predicted pIC50 = 6.042) and MSI (predicted pIC50 = 6.001) whose interactions with the D chain of modelled human tubulin protein were evaluated in detail. A toxicity evaluation resulted in MSI being less toxic in comparison to HPI. Conclusions The study provides an insight into the crucial structural requirements and the necessary chemical substitutions required for the arylthioindole moiety to exhibit enhanced inhibitory activity against human tubulin. The two reported compounds HPI and MSI showed promising anti cancer activities and thus can be considered as potent leads against cancer. The toxicity evaluation of these compounds suggests that MSI is a promising

  15. Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

    PubMed

    Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

    2007-01-01

    Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method

  16. Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

    PubMed

    Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi

    2015-01-01

    The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

  17. Attenuation of the Atmospheric Migration Ability of Polychlorinated Naphthalenes (PCN-2) Based on Three-dimensional QSAR Models with Full Factor Experimental Design.

    PubMed

    Gu, Wenwen; Chen, Ying; Li, Yu

    2017-08-01

    Based on the experimental subcooled liquid vapor pressures (P L ) of 17 polychlorinated naphthalene (PCN) congeners, one type of three-dimensional quantitative structure-activity relationship (3D-QSAR) models, comparative molecular similarity indices analysis (CoMSIA), was constructed with Sybyl software. Full factor experimental design was used to obtain the final regulation scheme for PCN, and then carry out modification of PCN-2 to significantly lower its P L . The contour maps of CoMSIA model showed that the migration ability of PCN decreases when the Cl atoms at the 2-, 3-, 4-, 5-, 6-, 7- and 8-positions of PCNs are replaced by electropositive groups. After modification of PCN-2, 12 types of new modified PCN-2 compounds were obtained with lnP L values two orders of magnitude lower than that of PCN-2. In addition, there are significant differences between the calculated total energies and energy gaps of the new modified compounds and those of PCN-2.

  18. Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study.

    PubMed

    Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B

    2015-01-01

    Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.

  19. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

    PubMed

    Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

    2016-06-01

    The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

  20. Prediction of Physicochemical Properties of Energetic Materials for Identification of Treatment Technologies for Waste Streams

    DTIC Science & Technology

    2010-11-01

    estimate the pharmacokinetics of potential drugs (Horning and Klamt 2005). QSPR/ QSARs also have potential applications in the fuel science field...group contribution methods, and (2) quantitative structure-property/activity relationships (QSPR/ QSAR ). The group contribution methods are primarily...development of QSPR/ QSARs is the identification of the ap- propriate set of descriptors that allow the desired attribute of the compound to be adequately

  1. Antitumor evaluation and 3D-QSAR studies of a new series of the spiropyrroloquinoline isoindolinone/aza-isoindolinone derivatives by comparative molecular field analysis (CoMFA).

    PubMed

    Sadeghzadeh, Masoud; Salahinejad, Maryam; Zarezadeh, Nahid; Ghandi, Mehdi; Baghery, Maryam Keshavarz

    2017-11-01

    In current study, antitumor activity of two series of the newly synthesized spiropyrroloquinoline isoindolinone and spiropyrroloquinoline aza-isoindolinone scaffolds was evaluated against three human breast normal and cancer cell lines (MCF-10A, MCF-7 and SK-BR-3) and compared with cytotoxicity values of doxorubicin and colchicine as the standard drugs. It was found that several compounds were endowed with cytotoxicity in the low micromolar range. Among these two series, compounds 6i, 6j, 6k and 7l, 7m, 7n, 7o containing 3-ethyl-1H-indole moiety were found to be highly effective against both cancer cell lines ranging from [Formula: see text] to [Formula: see text] in comparison with the corresponding analogs. Compared with human cancer cells, the most potent compounds did not show high cytotoxicity against human breast normal MCF-10A cells. Generally, most of the evaluated compounds 6a-l and 7a-o series showed more antitumor activity against SK-BR-3 than MCF-7 cells. Moreover, comparative molecular field analysis (CoMFA) as a popular tools of three-dimensional quantitative structure-activity relationship (3D-QSAR) studies was carried out on 27 spiropyrroloquinolineisoindolinone and spiropyrroloquinolineaza-isoindolinone derivatives with antitumor activity against on SK-BR-3 cells. The obtained CoMFA models showed statistically excellent performance, which also possessed good predictive ability for an external test set. The results confirm the important effect of molecular steric and electrostatic interactions of these compounds on in vitro cytotoxicity against SK-BR-3.

  2. Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework.

    PubMed

    Villaverde, Juan José; Sevilla-Morán, Beatriz; López-Goti, Carmen; Alonso-Prados, José Luis; Sandín-España, Pilar

    2018-09-01

    The European market for pesticides is currently legislated through the well-developed Regulation (EC) No. 1107/2009. This regulation promotes the competitiveness of European agriculture, recognizing the necessity of safe pesticides for human and animal health and the environment to protect crops against pests, diseases and weeds. In this sense, nanotechnology can provide a tremendous opportunity to achieve a more rational use of pesticides. However, the lack of information regarding nanopesticides and their fate and behavior in the environment and their effects on human and animal health is inhibiting rapid nanopesticide incorporation into European Union agriculture. This review analyzes the recent state of knowledge on nanopesticide risk assessment, highlighting the challenges that need to be overcame to accelerate the arrival of these new tools for plant protection to European agricultural professionals. Novel nano-Quantitative Structure-Activity/Structure-Property Relationship (nano-QSAR/QSPR) tools for risk assessment are analyzed, including modeling methods and validation procedures towards the potential of these computational instruments to meet the current requirements for authorization of nanoformulations. Future trends on these issues, of pressing importance within the context of the current European pesticide legislative framework, are also discussed. Standard protocols to make high-quality and well-described datasets for the series of related but differently sized nanoparticles/nanopesticides are required. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. A novel method to estimate the affinity of HLA-A∗0201 restricted CTL epitope

    NASA Astrophysics Data System (ADS)

    Xu, Yun-sheng; Lin, Yong; Zhu, Bo; Lin, Zhi-hua

    2009-02-01

    A set of 70 peptides with affinity for the class I MHC HLA-A∗0201 molecule was subjected to quantitative structure-affinity relationship studies based on the SCORE function with good results ( r2 = 0.6982, RMS = 0.280). Then the 'leave-one-out' cross-validation (LOO-CV) and an outer test set including 18 outer samples were used to validate the QSAR model. The results of the LOO-CV were q2 = 0.6188, RMS = 0.315, and the results of outer test set were r2 = 0.5633, RMS = 0.2292. All these show that the QSAR model has good predictability. Statistical analysis showed that the hydrophobic and hydrogen bond interaction played a significant role in peptide-MHC molecule binding. The study also provided useful information for structure modification of CTL epitope, and laid theoretical base for molecular design of therapeutic vaccine.

  4. Synthesis and exploration of QSAR model of 2-methyl-3-[2-(2-methylprop-1-en-1-yl)-1H-benzimidazol-1-yl]pyrimido[1,2-a]benzimidazol-4(3H)-one as potential antibacterial agents.

    PubMed

    Sharma, Pratibha; Kumar, Ashok; Sharma, Manisha; Singh, Jitendra; Bandyopadhyay, Prabal; Sathe, Manisha; Kaushik, M P

    2012-04-01

    Present communication deals with the synthesis of novel 2-methyl-3-[2-(2-methylprop-1-en-1-yl)-1H-benzimidazol-1-yl]pyrimido[1,2-a]benzimidazol-4(3H)-one derivatives under phase transfer catalysis (PTC) conditions using benzyl triethyl ammonium chloride (BTEAC) as PTC. It also elicits the studies on in vitro antimicrobial evaluation of synthesized compounds against a representative genera of gram-negative and gram-positive bacteria i.e., Bacillus subtilis, Staphylococcus aureus, Pseudomonas diminuta and Escherichia coli. All the compounds have been found to manifest profound antimicrobial activity. Moreover, extensive quantitative structure-activity relationship (QSAR) studies have been performed to deduce a correlation between molecular descriptors under consideration and the elicited biological activity. A tri-parametric QSAR model has been generated upon rigorous statistical treatment.

  5. 3D-QSAR and Molecular Docking Studies on Derivatives of MK-0457, GSK1070916 and SNS-314 as Inhibitors against Aurora B Kinase

    PubMed Central

    Zhang, Baidong; Li, Yan; Zhang, Huixiao; Ai, Chunzhi

    2010-01-01

    Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q2 = 0.605, r2pred = 0.826), (q2 = 0.52, r2pred = 0.798) and (q2 = 0.582, r2pred = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors. PMID:21151441

  6. 3D-QSAR and molecular docking studies on derivatives of MK-0457, GSK1070916 and SNS-314 as inhibitors against Aurora B kinase.

    PubMed

    Zhang, Baidong; Li, Yan; Zhang, Huixiao; Ai, Chunzhi

    2010-11-02

    Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q(2) = 0.605, r(2) (pred) = 0.826), (q(2) = 0.52, r(2) (pred) = 0.798) and (q(2) = 0.582, r(2) (pred) = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors.

  7. Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships.

    PubMed

    Liu, Zhichao; Kelly, Reagan; Fang, Hong; Ding, Don; Tong, Weida

    2011-07-18

    The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the

  8. Discovery of novel urokinase plasminogen activator (uPA) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis.

    PubMed

    Al-Sha'er, Mahmoud A; Khanfar, Mohammad A; Taha, Mutasem O

    2014-01-01

    Urokinase plasminogen activator (uPA)-a serine protease-is thought to play a central role in tumor metastasis and angiogenesis and, therefore, inhibition of this enzyme could be beneficial in treating cancer. Toward this end, we explored the pharmacophoric space of 202 uPA inhibitors using seven diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, we employed genetic algorithm-based quantitative structure-activity relationship (QSAR) analysis as a competition arena to select the best possible combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within the training inhibitors (r (2) 162 = 0.74, F-statistic = 64.30, r (2) LOO = 0.71, r (2) PRESS against 40 test inhibitors = 0.79). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the uPA binding pocket. This conclusion was supported by receiver operating characteristic (ROC) curve analyses of the QSAR-selected pharmacophores. Moreover, the three pharmacophores were comparable with binding interactions seen in crystallographic structures of bound ligands within the uPA binding pocket. We employed the resulting pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds. The captured hits were tested in vitro. Overall, our modeling workflow identified new low micromolar anti-uPA hits.

  9. Structural, Electronic and Qsar Properties of the Cyfluthrin Molecule:. a Theoretical AM1 and PM3 Treatment

    NASA Astrophysics Data System (ADS)

    Çalişir, Emine Deniz; Erkoç, Şakir

    Cyfluthrin is a synthetic cyano-containing pyrethroid insecticide that has both contact and stomach poison action. It is a nonsystemic chemical used to control cutworms, ants, silverfish, cockroaches, mosquitoes, tobacco budworm and many others. Its primary agricultural uses have been for control of chewing and sucking insects on crops such as cotton, turf, ornamentals, hops, cereal, corn, deciduous fruit, peanuts, potatoes, and other vegetables. Cyfluthrin is also used in public health situations and for structural pest control. The structural, vibrational, electronic and QSAR properties of the cyfluthrin molecule in gas phase have been investigated theoretically by performing molecular mechanics method by using MM+ force field, and semi-empirical molecular orbital AM1 and PM3 calculations. The geometry of the molecule has been optimized, infrared spectrum (vibrational modes and intensities) and the electronic properties of the molecule have been calculated in its ground state. According to PM3 calculation, heat of formation of cyfluthrin molecule is about -48.58 kcal/mol (exothermic), which shows that this molecule thermodynamically be stable. The HOMO energy level for this molecule is found to be -9.701 eV and the LUMO energy level is -0.660 eV giving rise to a gap of 9.041 eV, which also indicates that cyfluthrin is thermodynamically stable.

  10. Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

    PubMed

    Xu, G; Hughes-Oliver, J M; Brooks, J D; Yeatts, J L; Baynes, R E

    2013-01-01

    Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

  11. Design, synthesis, α-glucosidase inhibitory activity, molecular docking and QSAR studies of benzimidazole derivatives

    NASA Astrophysics Data System (ADS)

    Dinparast, Leila; Valizadeh, Hassan; Bahadori, Mir Babak; Soltani, Somaieh; Asghari, Behvar; Rashidi, Mohammad-Reza

    2016-06-01

    In this study the green, one-pot, solvent-free and selective synthesis of benzimidazole derivatives is reported. The reactions were catalyzed by ZnO/MgO containing ZnO nanoparticles as a highly effective, non-toxic and environmentally friendly catalyst. The structure of synthesized benzimidazoles was characterized using spectroscopic technics (FT-IR, 1HNMR, 13CNMR). Synthesized compounds were evaluated for their α-glucosidase inhibitory potential. Compounds 3c, 3e, 3l and 4n were potent inhibitors with IC50 values ranging from 60.7 to 168.4 μM. In silico studies were performed to explore the binding modes and interactions between enzyme and synthesized benzimidazoles. Developed linear QSAR model based on density and molecular weight could predict bioactivity of newly synthesized compounds well. Molecular docking studies revealed the availability of some hydrophobic interactions. In addition, the bioactivity of most potent compounds had good correlation with estimated free energy of binding (ΔGbinding) which was calculated according to docked best conformations.

  12. Molecular docking, 3D-QSAR and structural optimization on imidazo-pyridine derivatives dually targeting AT1 and PPARg.

    PubMed

    Zhang, Jun; Hao, Qing-Qing; Liu, Xin; Jing, Zhi; Jia, Wen-Qing; Wang, Shu-Qing; Xu, Wei-Ren; Cheng, Xian-Chao; Wang, Run-Ling

    2017-04-11

    Telmisartan, a bifunctional agent of blood pressure lowering and glycemia reduction, was previously reported to antagonize angiotensin II type 1 (AT1) receptor and partially activate peroxisome proliferator-activated receptor γ (PPARγ) simultaneously. Through the modification to telmisartan, researchers designed and obtained imidazo-\\pyridine derivatives with the IC50s of 0.49~94.1 nM against AT1 and EC50s of 20~3640 nM towards PPARγ partial activation. For minutely inquiring the interaction modes with the relevant receptor and analyzing the structure-activity relationships, molecular docking and 3D-QSAR (Quantitative structure-activity relationships) analysis of these imidazo-\\pyridines on dual targets were conducted in this work. Docking approaches of these derivatives with both receptors provided explicit interaction behaviors and excellent matching degree with the binding pockets. The best CoMFA (Comparative Molecular Field Analysis) models exhibited predictive results of q2=0.553, r2=0.954, SEE=0.127, r2pred=0.779 for AT1 and q2=0.503, r2=1.00, SEE=0.019, r2pred=0.604 for PPARγ, respectively. The contour maps from the optimal model showed detailed information of structural features (steric and electrostatic fields) towards the biological activity. Combining the bioisosterism with the valuable information from above studies, we designed six molecules with better predicted activities towards AT1 and PPARγ partial activation. Overall, these results could be useful for designing potential dual AT1 antagonists and partial PPARγ agonists.

  13. DFT/PCM, QTAIM, 1H NMR conformational studies and QSAR modeling of thirty-two anti-Leishmania amazonensis Morita-Baylis-Hillman Adducts

    NASA Astrophysics Data System (ADS)

    Filho, Edilson B. A.; Moraes, Ingrid A.; Weber, Karen C.; Rocha, Gerd B.; Vasconcellos, Mário L. A. A.

    2012-08-01

    Morita-Baylis-Hillman Adducts (MBHA) has been recently synthesized and bio-evaluated by our research group against Leishmania amazonensis, parasite that causes cutaneous and mucocutaneous leishmaniasis. We present here a theoretical conformational study of thirty-two leismanicidal MBHA by B3LYP/6-31+g(d) calculations with Polarized Continuum Model (PCM) to simulate water influence. Intramolecular Hydrogen Bonds (IHBs) indicated to control the most conformational preferences of MBHA. Quantum Theory Atoms in Molecules (QTAIM) calculations were able to characterize these interactions at Bond Critical Point level. Compounds presenting an unusual seven member IHB between NO2 group and hydroxyl moiety, supported by experimental spectroscopic data, showed a considerable improvement of biological activity (lower IC50 values). These results are in accordance to redox NO2 mechanism of action. Based on structural observations, some molecular descriptors were calculated and submitted to Quantitative Structure-Activity Relationship (QSAR) studies through the PLS Regression Method. These studies provided a model with good validation parameters values (R2 = 0.71, Q2 = 0.61 and Qext2 = 0.92).

  14. Prediction of Environmental Impact of High-Energy Materials with Atomistic Computer Simulations

    DTIC Science & Technology

    2010-11-01

    from a training set of compounds. Other methods include Quantitative Struc- ture-Activity Relationship ( QSAR ) and Quantitative Structure-Property...26 28 the development of QSPR/ QSAR models, in contrast to boiling points and critical parameters derived from empirical correlations, to improve...Quadratic Configuration Interaction Singles Doubles QSAR Quantitative Structure-Activity Relationship QSPR Quantitative Structure-Property

  15. Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations.

    PubMed

    Qian, Haiyan; Chen, Jiongjiong; Pan, Youlu; Chen, Jianzhong

    2016-09-19

    11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a potential target for the treatment of numerous human disorders, such as diabetes, obesity, and metabolic syndrome. In this work, molecular modeling studies combining molecular docking, 3D-QSAR, MESP, MD simulations and free energy calculations were performed on pyridine amides and 1,2,4-triazolopyridines as 11β-HSD1 inhibitors to explore structure-activity relationships and structural requirement for the inhibitory activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by docking strategy. The derived pharmacophoric features were further supported by MESP and Mulliken charge analyses using density functional theory. In addition, MD simulations and free energy calculations were employed to determine the detailed binding process and to compare the binding modes of inhibitors with different bioactivities. The binding free energies calculated by MM/PBSA showed a good correlation with the experimental biological activities. Free energy analyses and per-residue energy decomposition indicated the van der Waals interaction would be the major driving force for the interactions between an inhibitor and 11β-HSD1. These unified results may provide that hydrogen bond interactions with Ser170 and Tyr183 are favorable for enhancing activity. Thr124, Ser170, Tyr177, Tyr183, Val227, and Val231 are the key amino acid residues in the binding pocket. The obtained results are expected to be valuable for the rational design of novel potent 11β-HSD1 inhibitors.

  16. Combined 3D-QSAR, molecular docking, molecular dynamics simulation, and binding free energy calculation studies on the 5-hydroxy-2H-pyridazin-3-one derivatives as HCV NS5B polymerase inhibitors.

    PubMed

    Yu, Haijing; Fang, Yu; Lu, Xia; Liu, Yongjuan; Zhang, Huabei

    2014-01-01

    The NS5B RNA-dependent RNA polymerase (RdRP) is a promising therapeutic target for developing novel anti-hepatitis C virus (HCV) drugs. In this work, a combined molecular modeling study was performed on a series of 193 5-hydroxy-2H-pyridazin-3-one derivatives as inhibitors of HCV NS5B Polymerase. The best 3D-QSAR models, including CoMFA and CoMSIA, are based on receptor (or docking). Furthermore, a 40-ns molecular dynamics (MD) simulation and binding free energy calculations using docked structures of NS5B with ten compounds, which have diverse structures and pIC50 values, were employed to determine the detailed binding process and to compare the binding modes of the inhibitors with different activities. On one side, the stability and rationality of molecular docking and 3D-QSAR results were validated by MD simulation. The binding free energies calculated by the MM-PBSA method gave a good correlation with the experimental biological activity. On the other side, by analyzing some differences between the molecular docking and the MD simulation results, we can find that the MD simulation could also remedy the defects of molecular docking. The analyses of the combined molecular modeling results have identified that Tyr448, Ser556, and Asp318 are the key amino acid residues in the NS5B binding pocket. The results from this study can provide some insights into the development of novel potent NS5B inhibitors. © 2013 John Wiley & Sons A/S.

  17. Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method

    NASA Astrophysics Data System (ADS)

    Gee, Veronica M. W.; Wong, Fiona S. L.; Ramachandran, Lalitha; Sethi, Gautam; Kumar, Alan Prem; Yap, Chun Wei

    2014-11-01

    Peroxisome proliferator-activated receptor-gamma (PPARγ) plays a critical role in lipid and glucose homeostasis. It is the target of many drug discovery studies, because of its role in various disease states including diabetes and cancer. Thiazolidinediones, a synthetic class of agents that work by activation of PPARγ, have been used extensively as insulin-sensitizers for the management of type 2 diabetes. In this study, a combination of QSAR and docking methods were utilised to perform virtual screening of more than 25 million compounds in the ZINC library. The QSAR model was developed using 1,517 compounds and it identified 42,378 potential PPARγ agonists from the ZINC library, and 10,000 of these were selected for docking with PPARγ based on their diversity. Several steps were used to refine the docking results, and finally 30 potentially highly active ligands were identified. Four compounds were subsequently tested for their in vitro activity, and one compound was found to have a K i values of <5 μM.

  18. Drug interaction study of flavonoids toward CYP3A4 and their quantitative structure activity relationship (QSAR) analysis for predicting potential effects.

    PubMed

    Li, Yannan; Ning, Jing; Wang, Yan; Wang, Chao; Sun, Chengpeng; Huo, Xiaokui; Yu, Zhenlong; Feng, Lei; Zhang, Baojing; Tian, Xiangge; Ma, Xiaochi

    2018-05-09

    The high risk of herb-drug interactions (HDIs) mediated by the herbal medicines and dietary supplements which containing abundant flavonoids had become more and more frequent in our daily life. In our study, the inhibition activities of 44 different structures of flavonoids toward human CYPs were systemically evaluated for the first time. According to our results, a remarkable structure-dependent inhibition behavior toward CYP3A4 was observed in vitro. Some flavonoids such as licoflavone (12) and irilone (30) exhibited the selective inhibition toward CYP3 A4 rather than other major human CYPs. To illustrate the interaction mechanism, the inhibition kinetics of various compounds was further performed. Sophoranone (1), apigenin (10), baicalein (11), 5,4'-dihydroxy-3,6,7,8,3'-pentamethoxyflavone (15), myricetin (23) and kushenol K (38) remarkably inhibited the CYP3 A4-catalyzed bufalin 5'-hydroxylation reaction, with K i values of 2.17 ± 0.29, 6.15 ± 0.39, 9.18 ± 3.40, 2.30 ± 0.36, 5.00 ± 2.77 and 1.35 ± 0.25 μM, respectively. Importantly, compounds 1, 11, 15, 23 and 38 could significantly inhibit the metabolism of some clinical drugs in vitro, and these drug-drug interactions (DDIs) of myricetin (23) or kushenol K (38) with clinical drug diazepam were further verified in human primary hepatocytes, respectively. Finally, a quantitative structure-activity relationship (QSAR) of flavonoids with their inhibitory effects toward CYP3 A4 was established using computational methods. Our findings illustrated the high risk of herb-drug interactions (HDIs) caused by flavonoids and revealed the vital structures requirement of natural flavonoids for the HDIs with clinical drugs eliminated by CYP3 A4. Our research provided the useful guidance to safely and rationally use herbal medicines and dietary supplements containing rich natural flavonoids components. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. 3D-QSAR and docking studies of 3-Pyridine heterocyclic derivatives as potent PI3K/mTOR inhibitors

    NASA Astrophysics Data System (ADS)

    Yang, Wenjuan; Shu, Mao; Wang, Yuanqiang; Wang, Rui; Hu, Yong; Meng, Lingxin; Lin, Zhihua

    2013-12-01

    Phosphoinosmde-3-kinase/ mammalian target of rapamycin (PI3K/mTOR) dual inhibitors have attracted a great deal of interest as antitumor drugs research. In order to design and optimize these dual inhibitors, two types of 3D-quantitative structure-activity relationship (3D-QSAR) studies based on the ligand alignment and receptor alignment were applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). In the study based on ligands alignment, models of PI3K (CoMFA with r2, 0.770; q2, 0.622; CoMSIA with r2, 0.945; q2, 0.748) and mTOR (CoMFA with r2, 0.850; q2, 0.654; CoMSIA with r2, 0.983; q2, 0.676) have good predictability. And in the study based on receptor alignment, models of PI3K (CoMFA with r2, 0.745; q2, 0.538; CoMSIA with r2, 0.938; q2, 0.630) and mTOR (CoMFA with r2, 0.977; q2, 0.825; CoMSIA with r2, 0.985; q2, 0.728) also have good predictability. 3D contour maps and docking results suggested different groups on the core parts of the compounds could enhance the biological activities. Finally, ten derivatives as potential candidates of PI3K/mTOR inhibitors with good predicted activities were designed.

  20. Merging Applicability Domains for in Silico Assessment of Chemical Mutagenicity

    DTIC Science & Technology

    2014-02-04

    molecular fingerprints as descriptors for developing quantitative structure−activity relationship ( QSAR ) models and defining applicability domains with...used to define and quantify an applicability domain for either method. The importance of using applicability domains in QSAR modeling cannot be...domain from roughly 80% to 90%. These results indicated that the proposed QSAR protocol constituted a highly robust chemical mutagenicity prediction

  1. MIANN models in medicinal, physical and organic chemistry.

    PubMed

    González-Díaz, Humberto; Arrasate, Sonia; Sotomayor, Nuria; Lete, Esther; Munteanu, Cristian R; Pazos, Alejandro; Besada-Porto, Lina; Ruso, Juan M

    2013-01-01

    Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.

  2. Tuning hERG out: Antitarget QSAR Models for Drug Development

    PubMed Central

    Braga, Rodolpho C.; Alves, Vinícius M.; Silva, Meryck F. B.; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H.

    2015-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg). PMID:24805060

  3. Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

    PubMed Central

    Kumar, Raj; Son, Minky; Bavi, Rohit; Lee, Yuno; Park, Chanin; Arulalapperumal, Venkatesh; Cao, Guang Ping; Kim, Hyong-ha; Suh, Jung-keun; Kim, Yong-seong; Kwon, Yong Jung; Lee, Keun Woo

    2015-01-01

    Aim: Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches. Methods: The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations. Results: The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer's randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10. Conclusion: Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors. PMID:26051108

  4. Molecular docking, 3D-QSAR and structural optimization on imidazo-pyridine derivatives dually targeting AT1 and PPARγ

    PubMed Central

    Zhang, Jun; Hao, Qing-Qing; Liu, Xin; Jing, Zhi; Jia, Wen-Qing; Wang, Shu-Qing; Xu, Wei-Ren; Cheng, Xian-Chao; Wang, Run-Ling

    2017-01-01

    Telmisartan, a bifunctional agent of blood pressure lowering and glycemia reduction, was previously reported to antagonize angiotensin II type 1 (AT1) receptor and partially activate peroxisome proliferator-activated receptor γ (PPARγ) simultaneously. Through the modification to telmisartan, researchers designed and obtained imidazo-\\pyridine derivatives with the IC50s of 0.49∼94.1 nM against AT1 and EC50s of 20∼3640 nM towards PPARγ partial activation. For minutely inquiring the interaction modes with the relevant receptor and analyzing the structure-activity relationships, molecular docking and 3D-QSAR (Quantitative structure-activity relationships) analysis of these imidazo-\\pyridines on dual targets were conducted in this work. Docking approaches of these derivatives with both receptors provided explicit interaction behaviors and excellent matching degree with the binding pockets. The best CoMFA (Comparative Molecular Field Analysis) models exhibited predictive results of q2=0.553, r2=0.954, SEE=0.127, r2pred=0.779 for AT1 and q2=0.503, r2=1.00, SEE=0.019, r2pred=0.604 for PPARγ, respectively. The contour maps from the optimal model showed detailed information of structural features (steric and electrostatic fields) towards the biological activity. Combining the bioisosterism with the valuable information from above studies, we designed six molecules with better predicted activities towards AT1 and PPARγ partial activation. Overall, these results could be useful for designing potential dual AT1 antagonists and partial PPARγ agonists. PMID:28445965

  5. GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

    PubMed

    Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

    2012-09-24

    Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

  6. Consensus QSAR model for identifying novel H5N1 inhibitors.

    PubMed

    Sharma, Nitin; Yap, Chun Wei

    2012-08-01

    Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training set were comparable, thus it was unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173,674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor.

  7. Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

    PubMed Central

    Prachayasittikul, Veda; Pingaew, Ratchanok; Worachartcheewan, Apilak; Sitthimonchai, Somkid; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

    2017-01-01

    A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors. PMID:28827987

  8. Pyridones as NNRTIs against HIV-1 mutants: 3D-QSAR and protein informatics

    NASA Astrophysics Data System (ADS)

    Debnath, Utsab; Verma, Saroj; Jain, Surabhi; Katti, Setu B.; Prabhakar, Yenamandra S.

    2013-07-01

    CoMFA and CoMSIA based 3D-QSAR of HIV-1 RT wild and mutant (K103, Y181C, and Y188L) inhibitory activities of 4-benzyl/benzoyl pyridin-2-ones followed by protein informatics of corresponding non-nucleoside inhibitors' binding pockets from pdbs 2BAN, 3MED, 1JKH, and 2YNF were analysed to discover consensus features of the compounds for broad-spectrum activity. The CoMFA/CoMSIA models indicated that compounds with groups which lend steric-cum-electropositive fields in the vicinity of C5, hydrophobic field in the vicinity of C3 of pyridone region and steric field in aryl region produce broad-spectrum anti-HIV-1 RT activity. Also, a linker rendering electronegative field between pyridone and aryl moieties is common requirement for the activities. The protein informatics showed considerable alteration in residues 181 and 188 characteristics on mutation. Also, mutants' isoelectric points shifted in acidic direction. The study offered fresh avenues for broad-spectrum anti-HIV-1 agents through designing new molecules seeded with groups satisfying common molecular fields and concerns of mutating residues.

  9. Quantitative structure-activity relationship: promising advances in drug discovery platforms.

    PubMed

    Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

    2015-12-01

    Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

  10. Deep neural nets as a method for quantitative structure-activity relationships.

    PubMed

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

  11. Prediction of biodegradability of aromatics in water using QSAR modeling.

    PubMed

    Cvetnic, Matija; Juretic Perisic, Daria; Kovacic, Marin; Kusic, Hrvoje; Dermadi, Jasna; Horvat, Sanja; Bolanca, Tomislav; Marin, Vedrana; Karamanis, Panaghiotis; Loncaric Bozic, Ana

    2017-05-01

    The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD 5 ) and chemical (COD) oxygen demand values determined for parent compounds ((BOD 5 /COD) 0 ), as well as for their reaction mixtures in half-life achieved by UV-C/H 2 O 2 process ((BOD 5 /COD) t1/2 ). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD 5 /COD) 0 and (BOD 5 /COD) t1/2 , respectively, according to the values of statistical parameters R 2 and Q 2 . Hence, 3-variable model predicting (BOD 5 /COD) 0 possessed R 2 =0.863 and Q 2 =0.799 for training set, and R 2 =0.710 for test set, while 5-variable model predicting (BOD 5 /COD) 1/2 possessed R 2 =0.886 and Q 2 =0.788 for training set, and R 2 =0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H 2 O 2 . Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Gaussian processes: a method for automatic QSAR modeling of ADME properties.

    PubMed

    Obrezanova, Olga; Csanyi, Gabor; Gola, Joelle M R; Segall, Matthew D

    2007-01-01

    In this article, we discuss the application of the Gaussian Process method for the prediction of absorption, distribution, metabolism, and excretion (ADME) properties. On the basis of a Bayesian probabilistic approach, the method is widely used in the field of machine learning but has rarely been applied in quantitative structure-activity relationship and ADME modeling. The method is suitable for modeling nonlinear relationships, does not require subjective determination of the model parameters, works for a large number of descriptors, and is inherently resistant to overtraining. The performance of Gaussian Processes compares well with and often exceeds that of artificial neural networks. Due to these features, the Gaussian Processes technique is eminently suitable for automatic model generation-one of the demands of modern drug discovery. Here, we describe the basic concept of the method in the context of regression problems and illustrate its application to the modeling of several ADME properties: blood-brain barrier, hERG inhibition, and aqueous solubility at pH 7.4. We also compare Gaussian Processes with other modeling techniques.

  13. Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

    NASA Astrophysics Data System (ADS)

    Ojha, Lokendra Kumar

    2017-07-01

    The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

  14. Analysis and prediction of structure-reactive toxicity relationships of substituted aromatic compounds

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Liu, Z.T.; Wang, L.S.; Chen, S.P.

    1996-12-31

    The fundamental differentiation of toxicity is between reactive and nonreactive toxicity. Reactive toxicity is associated with a specific mechanism for the reaction with an enzyme or inhibition of a metabolic pathway, and nonreactive toxicity is related directly to the quantity of toxicant acting upon the cell. The quantitative structure-activity relationships (QSARs) have been successfully used in the nonreactive toxicity, such as prediction of the toxicity of nonreactive compounds based on their solubility in the lipids of organisms. The elements of molecular structure that are most closely related to nonreactive toxicity are those that describe the partitioning of the toxicant intomore » the organism, while QSARs for the reactive toxicity are less common in the environmental toxicology literature. With the recent increase in the use of synthetic substituted benzenes as industrial chemicals, the accurate analysis of the effect of reactive toxic chemicals has become recognized with QSAR. For this purpose, we selected the fish (Carassias auratus) as the test organism, measured the acute toxicity of 50% lethal concentration (LC{sub 50}) of the chemicals and the adenosine triphosphate (ATP) content of the liver cells for the organism. These determined the relationships of the acute toxicity of some substituted benzenes with their physicochemical structural parameters. The effects on the ATP content was also compared to predict biological reactivities of the chemicals, so as to find some clues to explain the mode of mechanism of the toxicity. 17 refs., 1 tab.« less

  15. Imidazole derivatives as angiotensin II AT1 receptor blockers: Benchmarks, drug-like calculations and quantitative structure-activity relationships modeling

    NASA Astrophysics Data System (ADS)

    Alloui, Mebarka; Belaidi, Salah; Othmani, Hasna; Jaidane, Nejm-Eddine; Hochlaf, Majdi

    2018-03-01

    We performed benchmark studies on the molecular geometry, electron properties and vibrational analysis of imidazole using semi-empirical, density functional theory and post Hartree-Fock methods. These studies validated the use of AM1 for the treatment of larger systems. Then, we treated the structural, physical and chemical relationships for a series of imidazole derivatives acting as angiotensin II AT1 receptor blockers using AM1. QSAR studies were done for these imidazole derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of imidazole derivatives. Results validate the derived QSAR model.

  16. Machine learning methods in chemoinformatics

    PubMed Central

    Mitchell, John B O

    2014-01-01

    Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

  17. Three-dimensional quantitative structure-activity relationship studies on c-Src inhibitors based on different docking methods.

    PubMed

    Bairy, Santhosh Kumar; Suneel Kumar, B V S; Bhalla, Joseph Uday Tej; Pramod, A B; Ravikumar, Muttineni

    2009-04-01

    c-Src kinase play an important role in cell growth and differentiation and its inhibitors can be useful for the treatment of various diseases, including cancer, osteoporosis, and metastatic bone disease. Three dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on quinazolin derivatives inhibiting c-Src kinase. Molecular field analysis (MFA) models with four different alignment techniques, namely, GLIDE, GOLD, LIGANDFIT and Least squares based methods were developed. glide based MFA model showed better results (Leave one out cross validation correlation coefficient r(2)(cv) = 0.923 and non-cross validation correlation coefficient r(2)= 0.958) when compared with other models. These results help us to understand the nature of descriptors required for activity of these compounds and thereby provide guidelines to design novel and potent c-Src kinase inhibitors.

  18. New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

    PubMed

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2004-11-21

    Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).

  19. In silico environmental chemical science: properties and processes from statistical and computational modelling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Tratnyek, Paul G.; Bylaska, Eric J.; Weber, Eric J.

    2017-01-01

    Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs usingmore » descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.« less

  20. QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

    NASA Astrophysics Data System (ADS)

    Zheng, Mingyue; Yu, Kunqian; Liu, Hong; Luo, Xiaomin; Chen, Kaixian; Zhu, Weiliang; Jiang, Hualiang

    2006-09-01

    The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q 2 of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.

  1. QSAR study on the antimalarial activity of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors.

    PubMed

    Hou, X; Chen, X; Zhang, M; Yan, A

    2016-01-01

    Plasmodium falciparum, the most fatal parasite that causes malaria, is responsible for over one million deaths per year. P. falciparum dihydroorotate dehydrogenase (PfDHODH) has been validated as a promising drug development target for antimalarial therapy since it catalyzes the rate-limiting step for DNA and RNA biosynthesis. In this study, we investigated the quantitative structure-activity relationships (QSAR) of the antimalarial activity of PfDHODH inhibitors by generating four computational models using a multilinear regression (MLR) and a support vector machine (SVM) based on a dataset of 255 PfDHODH inhibitors. All the models display good prediction quality with a leave-one-out q(2) >0.66, a correlation coefficient (r) >0.85 on both training sets and test sets, and a mean square error (MSE) <0.32 on training sets and <0.37 on test sets, respectively. The study indicated that the hydrogen bonding ability, atom polarizabilities and ring complexity are predominant factors for inhibitors' antimalarial activity. The models are capable of predicting inhibitors' antimalarial activity and the molecular descriptors for building the models could be helpful in the development of new antimalarial drugs.

  2. Locally Weighted Learning Methods for Predicting Dose-Dependent Toxicity with Application to the Human Maximum Recommended Daily Dose

    DTIC Science & Technology

    2012-09-10

    Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States ABSTRACT: Toxicological ...species. Thus, it is more advantageous to predict the toxicological effects of a compound on humans directly from the human toxicological data of related...compounds. However, many popular quantitative structure−activity relationship ( QSAR ) methods that build a single global model by fitting all training

  3. Structure-Activity Relationships Based on 3D-QSAR CoMFA/CoMSIA and Design of Aryloxypropanol-Amine Agonists with Selectivity for the Human β3-Adrenergic Receptor and Anti-Obesity and Anti-Diabetic Profiles.

    PubMed

    Lorca, Marcos; Morales-Verdejo, Cesar; Vásquez-Velásquez, David; Andrades-Lagos, Juan; Campanini-Salinas, Javier; Soto-Delgado, Jorge; Recabarren-Gajardo, Gonzalo; Mella, Jaime

    2018-05-16

    The wide tissue distribution of the adrenergic β3 receptor makes it a potential target for the treatment of multiple pathologies such as diabetes, obesity, depression, overactive bladder (OAB), and cancer. Currently, there is only one drug on the market, mirabegron, approved for the treatment of OAB. In the present study, we have carried out an extensive structure-activity relationship analysis of a series of 41 aryloxypropanolamine compounds based on three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. This is the first combined comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study in a series of selective aryloxypropanolamines displaying anti-diabetes and anti-obesity pharmacological profiles. The best CoMFA and CoMSIA models presented values of r ² ncv = 0.993 and 0.984 and values of r ² test = 0.865 and 0.918, respectively. The results obtained were subjected to extensive external validation ( q ², r ², r ² m , etc.) and a final series of compounds was designed and their biological activity was predicted (best pEC 50 = 8.561).

  4. New potent and selective cytochrome P450 2B6 (CYP2B6) inhibitors based on three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis

    PubMed Central

    Korhonen, L E; Turpeinen, M; Rahnasto, M; Wittekindt, C; Poso, A; Pelkonen, O; Raunio, H; Juvonen, R O

    2007-01-01

    Background and purpose: The cytochrome P450 2B6 (CYP2B6) enzyme metabolises a number of clinically important drugs. Drug-drug interactions resulting from inhibition or induction of CYP2B6 activity may cause serious adverse effects. The aims of this study were to construct a three-dimensional structure-activity relationship (3D-QSAR) model of the CYP2B6 protein and to identify novel potent and selective inhibitors of CYP2B6 for in vitro research purposes. Experimental approach: The inhibition potencies (IC50 values) of structurally diverse chemicals were determined with recombinant human CYP2B6 enzyme. Two successive models were constructed using Comparative Molecular Field Analysis (CoMFA). Key results: Three compounds proved to be very potent and selective competitive inhibitors of CYP2B6 in vitro (IC50<1 μM): 4-(4-chlorobenzyl)pyridine (CBP), 4-(4-nitrobenzyl)pyridine (NBP), and 4-benzylpyridine (BP). A complete inhibition of CYP2B6 activity was achieved with 0.1 μM CBP, whereas other CYP-related activities were not affected. Forty-one compounds were selected for further testing and construction of the final CoMFA model. The created CoMFA model was of high quality and predicted accurately the inhibition potency of a test set (n=7) of structurally diverse compounds. Conclusions and implications: Two CoMFA models were created which revealed the key molecular characteristics of inhibitors of the CYP2B6 enzyme. The final model accurately predicted the inhibitory potencies of several structurally unrelated compounds. CBP, BP and NBP were identified as novel potent and selective inhibitors of CYP2B6 and CBP especially is a suitable inhibitor for in vitro screening studies. PMID:17325652

  5. Rank Order Entropy: why one metric is not enough

    PubMed Central

    McLellan, Margaret R.; Ryan, M. Dominic; Breneman, Curt M.

    2011-01-01

    known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a “rank order entropy” evaluation, or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes, and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes, and provides confidence in the use of a particular model within a specific domain of applicability. PMID:21875058

  6. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds

    NASA Astrophysics Data System (ADS)

    Marrero-Ponce, Yovani; Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M.; García-Domenech, Ramón; De Julián-Ortiz, Jesus Vicente; Montero, Alina; Escario, José Antonio; Barrio, Alicia Gómez; Pereira, David Montero; Nogal, Juan José; Grau, Ricardo; Torrens, Francisco; Vogel, Christian; Arán, Vicente J.

    2008-08-01

    Trichomonas vaginalis ( Tv) is the causative agent of the most common, non-viral, sexually transmitted disease in women and men worldwide. Since 1959, metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However, resistance to MTZ in some patients and the great cost associated with the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, are able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in the training (test) sets, respectively. These results validate the models for their use in the ligand-based virtual screening. In addition, they show large Matthews' correlation coefficients ( C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidences the robustness of the obtained models. Later, both models are applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classify 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is the most important criterion for validating the models. Besides, these classification functions are applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds are synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions, since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Moreover, out of the seven compounds that are screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) show

  7. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds.

    PubMed

    Marrero-Ponce, Yovani; Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M; García-Domenech, Ramón; De Julián-Ortiz, Jesus Vicente; Montero, Alina; Escario, José Antonio; Barrio, Alicia Gómez; Pereira, David Montero; Nogal, Juan José; Grau, Ricardo; Torrens, Francisco; Vogel, Christian; Arán, Vicente J

    2008-08-01

    Trichomonas vaginalis (Tv) is the causative agent of the most common, non-viral, sexually transmitted disease in women and men worldwide. Since 1959, metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However, resistance to MTZ in some patients and the great cost associated with the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, are able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in the training (test) sets, respectively. These results validate the models for their use in the ligand-based virtual screening. In addition, they show large Matthews' correlation coefficients (C) of 0.78 (0.71) and 0.65 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidences the robustness of the obtained models. Later, both models are applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classify 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is the most important criterion for validating the models. Besides, these classification functions are applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds are synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions, since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Moreover, out of the seven compounds that are screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) show

  8. 3D-QSAR pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation toward identifying lead compounds for NS2B-NS3 protease inhibitors.

    PubMed

    Luo, Pei H; Zhang, Xuan R; Huang, Lan; Yuan, Lun; Zhou, Xang Z; Gao, X; Li, Ling S

    2017-10-01

    NS2B-NS3 protease has been identified to serve as lead drug design target due to its significant role in West Nile viral (WNV) and dengue virus (DENV) reproduction and replication. There are currently no approved chemotherapeutic drugs and effective vaccines to inhibit DENV and WNV infections. In this work, 3D-QSAR pharmacophore model has been developed to discover potential inhibitory candidates. Validation through Fischer's model and decoy test indicate that the developed 3D pharmacophore model is highly predictive for DENV inhibitors, which was then employed to screen ZINC chemical library to obtain reasonable hits. Following ADMET filtering, 15 hits were subjected to further filter through molecular docking and CoMFA modeling. Finally, top three hits were identified as lead compounds or potential inhibitory candidates with IC 50 values of ∼0.4637 µM and fitness of ∼57.73. It is implied from CoMFA modeling that substituents at the side site of benzotriazole such as a p-nitro group (e.g. biphenyl head) and a carbonyl (e.g. carboxylate function) at the side site of furan or amino group may improve bioactivity of ZINC85645245, respectively. Molecular dynamics simulations (MDS) were performed to discover new interactions and reinforce the binding modes from docking for the hits also. The QSAR and MDS results obtained from this work should be useful in determining structural requirements for inhibitor development as well as in designing more potential inhibitors for NS2B-NS3 protease.

  9. CoMFA and CoMSIA 3D-QSAR studies on S(6)-(4-nitrobenzyl)mercaptopurine riboside (NBMPR) analogs as inhibitors of human equilibrative nucleoside transporter 1 (hENT1).

    PubMed

    Gupte, Amol; Buolamwini, John K

    2009-01-15

    3D-QSAR (CoMFA and CoMSIA) studies were performed on human equlibrative nucleoside transporter (hENT1) inhibitors displaying K(i) values ranging from 10,000 to 0.7nM. Both CoMFA and CoMSIA analysis gave reliable models with q2 values >0.50 and r2 values >0.92. The models have been validated for their stability and robustness using group validation and bootstrapping techniques and for their predictive abilities using an external test set of nine compounds. The high predictive r2 values of the test set (0.72 for CoMFA model and 0.74 for CoMSIA model) reveals that the models can prove to be a useful tool for activity prediction of newly designed nucleoside transporter inhibitors. The CoMFA and CoMSIA contour maps identify features important for exhibiting good binding affinities at the transporter, and can thus serve as a useful guide for the design of potential equilibrative nucleoside transporter inhibitors.

  10. Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

    PubMed Central

    Shahlaei, M.; Saghaie, L.

    2014-01-01

    A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

  11. Computer-aided design, synthesis and biological assay of p-methylsulfonamido phenylethylamine analogues.

    PubMed

    Liu, H; Ji, M; Jiang, H; Liu, L; Hua, W; Chen, K; Ji, R

    2000-10-02

    Class III antiarrhythmic agents selectively delay the effective refractory period (ERP) and increase the transmembrance action potential duration (APD). Based on our previous studies, a set of 17 methylsulfonamido phenylethylamine analogues were investigated by 3D-QSAR techniques of CoMFA and CoMSIA. The 3D-QSAR models proved a good predictive ability, and could describe the steric, electrostatic and hydrophobic requirements for recognition forces of the receptor site. According to the clues provided by this 3D-QSAR analysis, we designed and synthesized a series of new analogues of methanesulfonamido phenylethylamine (VIa-i). Pharmacological assay indicated that the effective concentrations of delaying the functional refractory period (FRP) 10ms of these new compounds have a good correlation with the 3D-QSAR predicted values. It is remarkable that the maximal percent change of delaying FRP in microM of compound VIc is much higher than that of dofetilide. The results showed that the 3D-QSAR models are reliable.

  12. QSAR analyses of conformationally restricted 1,5-diaryl pyrazoles as selective COX-2 inhibitors: application of connection table representation of ligands.

    PubMed

    Prasanna, S; Manivannan, E; Chaturvedi, S C

    2005-04-15

    As a part of our continuing efforts in discerning the structural and physicochemical requirements for selective COX-2 over COX-1 inhibition among the fused pyrazole ring systems, herein we report the QSAR analyses of the title compounds. The conformational flexibility of the title compounds was examined using a simple connection table representation. The conformational investigation was aided by calculating a connection table parameter called fraction of rotable bonds, b_rotR encompassing the number of rotable bonds and b_count, the number of bonds including implicit hydrogens of each ligand. The hydrophobic and steric correlation of the title compounds towards selective COX-2 inhibition was reported previously in one of our recent publications. In this communication, we attempt to calculate Wang-Ford charges of the non-hydrogen common atoms of AM1 optimized geometries of the title compounds. Owing to the partial conformational flexibility of title compounds, conformationally restricted and unrestricted descriptors were calculated from MOE. Correlation analysis of these 2D, 3D and Wang-Ford charges was accomplished by linear regression analysis. 2D molecular descriptor b_single, 3D molecular descriptors glob, std_dim3 showed significant contribution towards COX-2 inhibitory activity. Balaban J, a connectivity topological index showed a negative and positive contribution towards COX-1 and selective COX-2 over COX-1 inhibition, respectively. Wang-Ford charges calculated on C(7) showed a significant contribution towards COX-1 inhibitory activity whereas charges calculated on C(8) were crucial in governing the selectivity of COX-2 over COX-1 inhibition among these congeners.

  13. Estrogen Receptor Binding Affinity of Food Contact Material Components Estimated by QSAR.

    PubMed

    Sosnovcová, Jitka; Rucki, Marián; Bendová, Hana

    2016-09-01

    The presented work characterized components of food contact materials (FCM) with potential to bind to estrogen receptor (ER) and cause adverse effects in the human organism. The QSAR Toolbox, software application designed to identify and fill toxicological data gaps for chemical hazard assessment, was used. Estrogen receptors are much less of a lock-and-key interaction than highly specific ones. The ER is nonspecific enough to permit binding with a diverse array of chemical structures. There are three primary ER binding subpockets, each with different requirements for hydrogen bonding. More than 900 compounds approved as of FCM components were evaluated for their potential to bind on ER. All evaluated chemicals were subcategorized to five groups with respect to the binding potential to ER: very strong, strong, moderate, weak binder, and no binder to ER. In total 46 compounds were characterized as potential disturbers of estrogen receptor. Among the group of selected chemicals, compounds with high and even very high affinity to the ER binding subpockets were found. These compounds may act as gene activators and cause adverse effects in the organism, particularly during pregnancy and breast-feeding. It should be considered to carry out further in vitro or in vivo tests to confirm their potential to disturb the regulation of physiological processes in humans by abnormal ER signaling and subsequently remove these chemicals from the list of approved food contact materials. Copyright© by the National Institute of Public Health, Prague 2016

  14. Comparative pharmacodynamic analysis of imidazoline compounds using rat model of ocular mydriasis with a test of quantitative structure-activity relationships.

    PubMed

    Raczak-Gutknecht, Joanna; Nasal, Antoni; Frąckowiak, Teresa; Kornicka, Anita; Sączewski, Franciszek; Wawrzyniak, Renata; Kubik, Łukasz; Kaliszan, Roman

    2017-09-10

    Imidazol(in)e derivatives, having the chemical structure similar to clonidine, exert diverse pharmacological activities connected with their interactions with alpha2-adrenergic receptors, e.g. hypotension, bradycardia, sedation as well as antinociceptive, anxiolytic, antiarrhythmic, muscle relaxant and mydriatic effects. The mechanism of pupillary dilation observed after systemic administration of imidazol(in)es to rats, mice and cats depends on the stimulation of postsynaptic alpha2-adrenoceptors within the brain. It was proved that the central nervous system (CNS)-localized I1-imidazoline receptors are not engaged in those effects. It appeared interesting to analyze the CNS-mediated pharmacodynamics of imidazole(in)e agents in terms of their chromatographic and calculation chemistry-derived parameters. In the present study a systematic determination and comparative pharmacometric analysis of mydriatic effects in rats were performed on a series of 20 imidazol(in)e agents, composed of the well-known drugs and of the substances used in experimental pharmacology. The eye pupil dilatory activities of the compounds were assessed in anesthetized Wistar rats according to the established Koss method. Among twenty imidazol(in)e derivatives studied, 18 produced diverse dose-dependent mydriatic effects. In the quantitative structure-activity relationships (QSAR) analysis, the pharmacological data (half maximum mydriatic effect - ED 50 in μmol/kg) were considered along with the structural parameters of the agents from molecular modeling. The theoretically calculated lipophilicity parameters, CLOGP, of imidazol(in)es, as well as their lipophilicity parameters from HPLC, logk w , were also considered. The attempts to derive statistically significant QSAR equations for a full series of the agents under study were unsuccessful. However, for a subgroup of eight apparently structurally related imidazol(in)es a significant relationship between log(1/ED 50 ) and logk w values was

  15. Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

    PubMed Central

    Low, Yen S; Caster, Ola; Bergvall, Tomas; Fourches, Denis; Zang, Xiaoling; Norén, G Niklas; Rusyn, Ivan; Edwards, Ralph

    2016-01-01

    Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations. PMID:26499102

  16. 3D-QSAR and docking studies on 1-hydroxypyridin-2-one compounds as mutant isocitrate dehydrogenase 1 inhibitors

    NASA Astrophysics Data System (ADS)

    Wang, Zhenya; Chang, Yiqun; Han, Yushui; Liu, Kangjia; Hou, Jinsong; Dai, Chengli; Zhai, Yuanhao; Guo, Jialiang; Sun, Pinghua; Lin, Jing; Chen, Weimin

    2016-11-01

    Mutation of isocitrate dehydrogenase 1 (IDH1) which is frequently found in certain cancers such as glioma, sarcoma and acute myeloid leukemia, has been proven to be a potent drug target for cancer therapy. In silico methodologies such as 3D-QSAR and molecular docking were performed to explore compounds with better mutant isocitrate dehydrogenase 1 (MIDH1) inhibitory activity using a series of 40 newly reported 1-hydroxypyridin-2-one compounds as MIDH1 inhibitors. The satisfactory CoMFA and CoMSIA models obtained after internal and external cross-validation gave q2 values of 0.691 and 0.535, r2 values of 0.984 and 0.936, respectively. 3D contour maps generated from CoMFA and CoMSIA along with the docking results provided information about the structural requirements for better MIDH1 inhibitory activity. Based on the structure-activity relationship, 17 new potent molecules with better predicted activity than the most active compound in the literature have been designed.

  17. Computational methods in drug discovery

    PubMed Central

    Leelananda, Sumudu P

    2016-01-01

    The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

  18. Computational methods in drug discovery.

    PubMed

    Leelananda, Sumudu P; Lindert, Steffen

    2016-01-01

    The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

  19. Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models.

    PubMed

    Petoumenou, Maria I; Pizzo, Fabiola; Cester, Josep; Fernández, Alberto; Benfenati, Emilio

    2015-10-01

    The bioconcentration factor (BCF) is the ratio of the concentration of a chemical in an organism to the concentration in the surrounding environment at steady state. It is a valuable indicator of the bioaccumulation potential of a substance. BCF is an essential environmental property required for regulatory purposes within the Registration, Evaluation, Authorization and restriction of Chemicals (REACH) and Globally Harmonized System (GHS) regulations. In silico models for predicting BCF can facilitate the risk assessment for aquatic toxicology and reduce the cost and number of animals used. The aim of the present study was to examine the correlation of BCF data derived from the dossiers of registered chemicals submitted to the European Chemical Agency (ECHA) with the results of a battery of Quantitative Structure-Activity Relationship (QSAR). After data pruning, statistical analysis was performed using the predictions of the selected models. Results in terms of R(2) had low rating around 0.5 for the pruned dataset. The use of the model applicability domain index (ADI) led to an improvement of the performance for compounds falling within it. The variability of the experimental data and the use of different parameters to define the applicability domain can influence the performance of each model. All available information should be adapted to the requirements of the regulation to obtain a safe decision. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

    DOEpatents

    West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

    2012-05-29

    Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.