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Sample records for activity relationships qsars

  1. 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...

  2. 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.

  3. Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction

    PubMed Central

    Frimayanti, Neni; Yam, Mun Li; Lee, Hong Boon; Othman, Rozana; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.

    2011-01-01

    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. PMID:22272096

  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. Quantitative structure-activity relationship (QSAR) study of a series of benzimidazole derivatives as inhibitors of Saccharomyces cerevisiae.

    PubMed

    Podunavac-Kuzmanović, Sonja O; Cvetković, Dragoljub D; Jevrić, Lidija R; Uzelac, Natasa J

    2013-01-01

    A quantitative structure activity relationship (QSAR) has been carried out on a series of benzimidazole derivatives to identify the structural requirements for their inhibitory activity against yeast Saccharomyces cerevisiae. A multiple linear regression (MLR) procedure was used to model the relationships between various physicochemical, steric, electronic, and structural molecular descriptors and antifungal activity of benzimidazole derivatives. The QSAR expressions were generated using a training set of 16 compounds and the predictive ability of the resulting models was evaluated against a test set of 8 compounds. The best QSAR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. Therefore, satisfactory relationships between antifungal activity and molecular descriptors were found. QSAR analysis reveals that lipophilicity descriptor (logP), dipole moment (DM) and surface area grid (SAG) govern the inhibitory activity of compounds studied against Saccharomyces cerevisiae.

  6. 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.

  7. Quantitative Structure‐activity Relationship (QSAR) Models for Docking Score Correction

    PubMed Central

    Yamasaki, Satoshi; Yasumatsu, Isao; Takeuchi, Koh; Kurosawa, Takashi; Nakamura, Haruki

    2016-01-01

    Abstract In order to improve docking score correction, we developed several structure‐based quantitative structure activity relationship (QSAR) models by protein‐drug docking simulations and applied these models to public affinity data. The prediction models used descriptor‐based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. The binding free energy that corresponded to the docking score was approximated by a weighted average of docking scores for multiple proteins, and we tried linear, weighted linear and polynomial regression models considering the compound similarities. In addition, we tried a combination of these regression models for individual data sets such as IC50, Ki, and %inhibition values. The cross‐validation results showed that the weighted linear model was more accurate than the simple linear regression model. Thus, the QSAR approaches based on the affinity data of public databases should improve docking scores. PMID:28001004

  8. Blood-brain barrier permeability mechanisms in view of quantitative structure-activity relationships (QSAR).

    PubMed

    Bujak, Renata; Struck-Lewicka, Wiktoria; Kaliszan, Michał; Kaliszan, Roman; Markuszewski, Michał J

    2015-04-10

    The goal of the present paper was to develop a quantitative structure-activity relationship (QSAR) method using a simple statistical approach, such as multiple linear regression (MLR) for predicting the blood-brain barrier (BBB) permeability of chemical compounds. The "best" MLR models, comprised logP and either molecular mass (M) or isolated atomic energy (E(isol)), tested on a structurally diverse set of 66 compounds, is characterized the by correlation coefficients (R) around 0.8. The obtained models were validated using leave-one-out (LOO) cross-validation technique and the correlation coefficient of leave-one-out- R(LOO)(2) (Q(2)) was at least 0.6. Analysis of a case from legal medicine demonstrated informative value of our QSAR model. To best authors' knowledge the present study is a first application of the developed QSAR models of BBB permeability to case from the legal medicine. Our data indicate that molecular energy-related descriptors, in combination with the well-known descriptors of lipophilicity may have a supportive value in predicting blood-brain distribution, which is of utmost importance in drug development and toxicological studies.

  9. Structure-hepatoprotective activity relationship study of sesquiterpene lactones: A QSAR analysis

    NASA Astrophysics Data System (ADS)

    Paukku, Yuliya; Rasulev, Bakhtiyor; Syrov, Vladimir; Khushbaktova, Zainab; Leszczynski, Jerzy

    This study has been carried out using quantitative structure-activity relationship analysis (QSAR) for 22 sesquiterpene lactones to correlate and predict their hepatoprotective activity. Sesquiterpenoids, the largest class of terpenoids, are a widespread group of substances occurring in various plant organisms. QSAR analysis was carried out using methods such as genetic algorithm for variables selection among generated and calculated descriptors and multiple linear regression analysis. Quantum-chemical calculations have been performed by density functional theory at B3LYP/6-311G(d, p) level for evaluation of electronic properties using reference geometries optimized by semi-empirical AM1 approach. Three models describing hepatoprotective activity values for series of sesquiterpene lactones are proposed. The obtained models are useful for description of sesquiterpene lactones hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted sesquiterpene lactones. The models obtained in our study show not only statistical significance, but also good predictive ability. The estimated predictive ability (rtest2) of these models lies within 0.942-0.969.

  10. Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species.

    PubMed Central

    Tong, W; Perkins, R; Strelitz, R; Collantes, E R; Keenan, S; Welsh, W J; Branham, W S; Sheehan, D M

    1997-01-01

    The recognition of adverse effects due to environmental endocrine disruptors in humans and wildlife has focused attention on the need for predictive tools to select the most likely estrogenic chemicals from a very large number of chemicals for subsequent screening and/or testing for potential environmental toxicity. A three-dimensional quantitative structure-activity relationship (QSAR) model using comparative molecular field analysis (CoMFA) was constructed based on relative binding affinity (RBA) data from an estrogen receptor (ER) binding assay using calf uterine cytosol. The model demonstrated significant correlation of the calculated steric and electrostatic fields with RBA and yielded predictions that agreed well with experimental values over the entire range of RBA values. Analysis of the CoMFA three-dimensional contour plots revealed a consistent picture of the structural features that are largely responsible for the observed variations in RBA. Importantly, we established a correlation between the predicted RBA values for calf ER and their actual RBA values for human ER. These findings suggest a means to begin to construct a more comprehensive estrogen knowledge base by combining RBA assay data from multiple species in 3D-QSAR based predictive models, which could then be used to screen untested chemicals for their potential to bind to the ER. Another QSAR model was developed based on classical physicochemical descriptors generated using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. The predictive ability of the CoMFA model was superior to the corresponding CODESSA model. Images Figure 2. Figure 3. Figure 4. Figure 5. PMID:9353176

  11. A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors

    PubMed Central

    Sadeghian-Rizi, Sedighe; Sakhteman, Amirhossein; Hassanzadeh, Farshid

    2016-01-01

    In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of V600EB-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site. PMID:28003837

  12. Development of acute toxicity quantitative structure activity relationships (QSAR) and their use in linear alkylbenzene sulfonate species sensitivity distributions.

    PubMed

    Belanger, Scott E; Brill, Jessica L; Rawlings, Jane M; Price, Brad B

    2016-07-01

    Linear Alkylbenzene Sulfonate (LAS) is high tonnage and widely dispersed anionic surfactant used by the consumer products sector. A range of homologous structures are used in laundry applications that differ primarily on the length of the hydrophobic alkyl chain. This research summarizes the development of a set of acute toxicity QSARs (Quantitative Structure Activity Relationships) for fathead minnows (Pimephales promelas) and daphnids (Daphnia magna, Ceriodaphnia dubia) using accepted test guideline approaches. A series of studies on pure chain length LAS from C10 to C14 were used to develop the QSARs and the robustness of the QSARs was tested by evaluation of two technical mixtures of differing compositions. All QSARs were high quality (R(2) were 0.965-0.997, p < 0.0001). Toxicity normalization employing QSARs is used to interpret a broader array of tests on LAS chain length materials to a diverse group of test organisms with the objective of developing Species Sensitivity Distributions (SSDs) for various chain lengths of interest. Mixtures include environmental distributions measured from exposure monitoring surveys of wastewater effluents, various commercial mixtures, or specific chain lengths. SSD 5th percentile hazardous concentrations (HC5s) ranged from 0.129 to 0.254 mg/L for wastewater effluents containing an average of 11.26-12 alkyl carbons. The SSDs are considered highly robust given the breadth of species (n = 19), use of most sensitive endpoints from true chronic studies and the quality of the underlying statistical properties of the SSD itself. The data continue to indicate a low hazard to the environment relative to expected environmental concentrations.

  13. 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

  14. 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

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

    NASA Astrophysics Data System (ADS)

    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.

  16. A structure-activity relationship study of catechol- O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods

    NASA Astrophysics Data System (ADS)

    Tervo, Anu J.; Nyrönen, Tommi H.; Rönkkö, Toni; Poso, Antti

    2003-12-01

    A panel of 92 catechol- O-methyltransferase (COMT) inhibitors was used to examine the molecular interactions affecting their biological activity. COMT inhibitors are used as therapeutic agents in the treatment of Parkinson's disease, but there are limitations in the currently marketed compounds due to adverse side effects. This study combined molecular docking methods with three-dimensional structure-activity relationships (3D QSAR) to analyse possible interactions between COMT and its inhibitors, and to incite the design of new inhibitors. Comparative molecular field analysis (CoMFA) and GRID/GOLPE models were made by using bioactive conformations from docking experiments, which yielded q2 values of 0.594 and 0.636, respectively. The docking results, the COMT X-ray structure, and the 3D QSAR models are in agreement with each other. The models suggest that an interaction between the inhibitor's catechol oxygens and the Mg2+ ion in the COMT active site is important. Both hydrogen bonding with Lys144, Asn170 and Glu199, and hydrophobic contacts with Trp38, Pro174 and Leu198 influence inhibitor binding. Docking suggests that a large R1 substituent of the catechol ring can form hydrophobic contacts with side chains of Val173, Leu198, Met201 and Val203 on the COMT surface. Our models propose that increasing steric volume of e.g. the diethylamine tail of entacapone is favourable for COMT inhibitory activity.

  17. A Structure-Activity Relationship Study of Imidazole-5-Carboxylic Acid Derivatives as Angiotensin II Receptor Antagonists Combining 2D and 3D QSAR Methods.

    PubMed

    Sharma, Mukesh C

    2016-03-01

    Two-dimensional (2D) and three-dimensional (3D) quantitative structure-activity relationship (QSAR) studies were performed for correlating the chemical composition of imidazole-5-carboxylic acid analogs and their angiotensin II [Formula: see text] receptor antagonist activity using partial least squares and k-nearest neighbor, respectively. For comparing the three different feature selection methods of 2D-QSAR, k-nearest neighbor models were used in conjunction with simulated annealing (SA), genetic algorithm and stepwise coupled with partial least square (PLS) showed variation in biological activity. The statistically significant best 2D-QSAR model having good predictive ability with statistical values of [Formula: see text] and [Formula: see text] was developed by SA-partial least square with the descriptors like [Formula: see text]count, 5Chain count, SdsCHE-index, and H-acceptor count, showing that increase in the values of these descriptors is beneficial to the activity. The 3D-QSAR studies were performed using the SA-PLS. A leave-one-out cross-validated correlation coefficient [Formula: see text] and predicate activity [Formula: see text] = 0.7226 were obtained. The information rendered by QSAR models may lead to a better understanding of structural requirements of substituted imidazole-5-carboxylic acid derivatives and also aid in designing novel potent antihypertensive molecules.

  18. Semisynthesis and quantitative structure-activity relationship (QSAR) study of some cholesterol-based hydrazone derivatives as insecticidal agents.

    PubMed

    Yang, Chun; Shao, Yonghua; Zhi, Xiaoyan; Huan, Qu; Yu, Xiang; Yao, Xiaojun; Xu, Hui

    2013-09-01

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, four series of novel cholesterol-based hydrazone derivatives were synthesized, and their insecticidal activity was tested against the pre-third-instar larvae of oriental armyworm, Mythimna separata (Walker) in vivo at 1mg/mL. All the derivatives showed the better insecticidal activity than their precursor cholesterol. Quantitative structure-activity relationship (QSAR) model demonstrated that six descriptors such as RDF085v, Mor06u, Mor11u, Dv, HATS0v and H-046, are likely to influence the insecticidal activity of these compounds. Among them, two important ones are the Mor06u and RDF085v.

  19. Estimating the persistence of organic contaminants in indirect potable reuse systems using quantitative structure activity relationship (QSAR).

    PubMed

    Lim, Seung Joo; Fox, Peter

    2012-09-01

    Predictions from the quantitative structure activity relationship (QSAR) model EPI Suite were modified to estimate the persistence of organic contaminants in indirect potable reuse systems. The modified prediction included the effects of sorption, biodegradation, and oxidation that may occur during sub-surface transport. A retardation factor was used to simulate the mobility of adsorbed compounds during sub-surface transport to a recovery well. A set of compounds with measured persistent properties during sub-surface transport was used to validate the results of the modifications to the predictions of EPI Suite. A comparison of the predicted values and measured values was done and the residual sum of the squares showed the importance of including oxidation and sorption. Sorption was the most important factor to include in predicting the fates of organic chemicals in the sub-surface environment.

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

    SciTech Connect

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

    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 distances 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.

  1. Utilization of quantitative structure-activity relationships (QSARs) in risk assessment: Alkylphenols

    SciTech Connect

    Beck, B.D.; Toole, A.P.; Callahan, B.G.; Siddhanti, S.K. )

    1991-12-01

    Alkylphenols are a class of environmentally pervasive compounds, found both in natural (e.g., crude oils) and in anthropogenic (e.g., wood tar, coal gasification waste) materials. Despite the frequent environmental occurrence of these chemicals, there is a limited toxicity database on alkylphenols. The authors have therefore developed a 'toxicity equivalence approach' for alkylphenols which is based on their ability to inhibit, in a specific manner, the enzyme cyclooxygenase. Enzyme-inhibiting ability for individual alkylphenols can be estimated based on the quantitative structure-activity relationship developed by Dewhirst (1980) and is a function of the free hydroxyl group, electron-donating ring substituents, and hydrophobic aromatic ring substituents. The authors evaluated the toxicological significance of cyclooxygenase inhibition by comparison of the inhibitory capacity of alkylphenols with the inhibitory capacity of acetylsalicylic acid, or aspirin, a compound whose low-level effects are due to cyclooxygenase inhibition. Since nearly complete absorption for alkylphenols and aspirin is predicted, based on estimates of hydrophobicity and fraction of charged molecules at gastrointestinal pHs, risks from alkylphenols can be expressed directly in terms of 'milligram aspirin equivalence,' without correction for absorption differences. They recommend this method for assessing risks of mixtures of alkylphenols, especially for those compounds with no chronic toxicity data.38 references.

  2. Quantitative Structure activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors using the k Nearest Neighbor Method and QSAR-based Database Mining

    NASA Astrophysics Data System (ADS)

    Medina-Franco, Jose Luis; Golbraikh, Alexander; Oloff, Scott; Castillo, Rafael; Tropsha, Alexander

    2005-04-01

    We have developed quantitative structure-activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest neighbor ( kNN) variable selection approach was used. This method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R 2 ( q 2) values ranging between 0.5 and 0.8 for the training sets and R 2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4- d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.

  3. Rational design of novel anti-microtubule agent (9-azido-noscapine) from quantitative structure activity relationship (QSAR) evaluation of noscapinoids.

    PubMed

    Santoshi, Seneha; Naik, Pradeep K; Joshi, Harish C

    2011-10-01

    An anticough medicine, noscapine [(S)-3-((R)4-methoxy-6-methyl-5,6,7,8-tetrahydro-[1,3]dioxolo[4,5-g]isoquinolin-5-yl)-6,7-dimethoxyiso-benzofuran-1(3H)-one], was discovered in the authors' laboratory as a novel type of tubulin-binding agent that mitigates polymerization dynamics of microtubule polymers without changing overall subunit-polymer equilibrium. To obtain systematic insight into the relationship between the structural framework of noscapine scaffold and its antitumor activity, the authors synthesized strategic derivatives (including two new ones in this article). The IC(50) values of these analogs vary from 1.2 to 56.0 µM in human acute lymphoblastic leukemia cells (CEM). Geometrical optimization was performed using semiempirical quantum chemical calculations at the 3-21G* level. Structures were in agreement with nuclear magnetic resonance analysis of molecular flexibility in solution and crystal structures. A genetic function approximation algorithm of variable selection was used to generate the quantitative structure activity relationship (QSAR) model. The robustness of the QSAR model (R(2) = 0.942) was analyzed by values of the internal cross-validated regression coefficient (R(2) (LOO) = 0.815) for the training set and determination coefficient (R(2) (test) = 0.817) for the test set. Validation was achieved by rational design of further novel and potent antitumor noscapinoid, 9-azido-noscapine, and reduced 9-azido-noscapine. The experimentally determined value of pIC(50) for both the compounds (5.585 M) turned out to be very close to predicted pIC(50) (5.731 and 5.710 M).

  4. 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...

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

    NASA Astrophysics Data System (ADS)

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; Ahmed, Lucky; Rasulev, Bakhtiyor; Avramopoulos, Aggelos; Papadopoulos, Manthos G.; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-09-01

    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 the 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.

  6. Synthesis and quantitative structure-activity relationship (QSAR) study of novel 4-acyloxypodophyllotoxin derivatives modified in the A and C rings as insecticidal agents.

    PubMed

    He, Shuzhen; Shao, Yonghua; Fan, Lingling; Che, Zhiping; Xu, Hui; Zhi, Xiaoyan; Wang, Juanjuan; Yao, Xiaojun; Qu, Huan

    2013-01-23

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, we have synthesized three series of novel 4-acyloxy compounds derived from podophyllotoxin modified in the A and C rings, which is isolated as the main secondary metabolite from the roots and rhizomes of Podophyllum hexandrum . Their insecticidal activity was preliminarily evaluated against the pre-third-instar larvae of Mythimna separata in vivo. Compound 9g displayed the best promising insecticidal activity. It revealed that cleavage of the 6,7-methylenedioxy group of podophyllotoxin will lead to a less active compound and that the C-4 position of podophyllotoxin was the important modification location. A quantitative structure-activity relationship (QSAR) model was developed by genetic algorithm combined with multiple linear regression (GA-MLR). For this model, the squared correlation coefficient (R(2)) is 0.914, the leave-one-out cross-validation correlation coefficient (Q(2)(LOO)) is 0.881, and the root-mean-square error (RMSE) is 0.024. Five descriptors, BEHm2, Mor14v, Wap, G1v, and RDF020e, are likely to influence the biological activity of these compounds. Among them, two important ones are BEHm2 and Mor14v. This study will pave the way for further design, structural modification, and development of podophyllotoxin derivatives as insecticidal agents.

  7. Synthesis and quantitative structure-activity relationship (QSAR) study of novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives as nematicidal agents.

    PubMed

    Che, Zhiping; Zhang, Shaoyong; Shao, Yonghua; Fan, Lingling; Xu, Hui; Yu, Xiang; Zhi, Xiaoyan; Yao, Xiaojun; Zhang, Rui

    2013-06-19

    In continuation of our program aimed at the discovery and development of natural-product-based pesticidal agents, 54 novel N-arylsulfonyl-3-acylindole arylcarbonyl hydrazone derivatives were prepared, and their structures were well characterized by ¹H NMR, ¹³C NMR, HRMS, ESI-MS, and mp. Their nematicidal activity was evaluated against that of the pine wood nematode, Bursaphelenchus xylophilus in vivo. Among all of the derivatives, especially V-12 and V-39 displayed the best promising nematicidal activity with LC₅₀ values of 1.0969 and 1.2632 mg/L, respectively. This suggested that introduction of R¹ and R² together as the electron-withdrawing substituents, R³ as the methyl group, and R⁴ as the phenyl with the electron-donating substituents could be taken into account for further preparation of these kinds of compounds as nematicidal agents. Six selected descriptors are a WHIM descriptor (E1m), two GETAWAY descriptors (R1m+ and R3m+), a Burden eigenvalues descriptor (BEHm8), and two edge-adjacency index descriptors (EEig05x and EEig13d). Quantitative structure-activity relationship (QSAR) studies demonstrated that the structural factors, such as molecular mass (a negative correlation with the bioactivity) and molecular polarity (a positive correlation with bioactivity), are likely to govern the nematicidal activities of these compounds. For this model, the correlation coefficient (R²(training set)), the leave-one-out cross-validation correlation coefficient (Q²(LOO)), and the 7-fold cross-validation correlation coefficient (Q²(7-fold)) were 0.791, 0.701, and 0.715, respectively. The external cross-validation correlation coefficient (Q²ext) and the root-mean-square error for the test set (RMSE(test set)) were 0.774 and 3.412, respectively. This study will pave the way for future design, structural modification, and development of indole derivatives as nematicidal agents.

  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. Synthesis and quantitative structure-activity relationship (QSAR) study of novel isoxazoline and oxime derivatives of podophyllotoxin as insecticidal agents.

    PubMed

    Wang, Yi; Shao, Yonghua; Wang, Yangyang; Fan, Lingling; Yu, Xiang; Zhi, Xiaoyan; Yang, Chun; Qu, Huan; Yao, Xiaojun; Xu, Hui

    2012-08-29

    In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, 33 isoxazoline and oxime derivatives of podophyllotoxin modified in the C and D rings were synthesized and their structures were characterized by Proton nuclear magnetic resonance ((1)H NMR), high-resolution mass spectrometry (HRMS), electrospray ionization-mass spectrometry (ESI-MS), optical rotation, melting point (mp), and infrared (IR) spectroscopy. The stereochemical configurations of compounds 5e, 5f, and 9f were unambiguously determined by X-ray crystallography. Their insecticidal activity was evaluated against the pre-third-instar larvae of northern armyworm, Mythimna separata (Walker), in vivo. Compounds 5e, 9c, 11g, and 11h especially exhibited more promising insecticidal activity than toosendanin, a commercial botanical insecticide extracted from Melia azedarach . A genetic algorithm combined with multiple linear regression (GA-MLR) calculation is performed by the MOBY DIGS package. Five selected descriptors are as follows: one two-dimensional (2D) autocorrelation descriptor (GATS4e), one edge adjacency indice (EEig06x), one RDF descriptor (RDF080v), one three-dimensional (3D) MoRSE descriptor (Mor09v), and one atom-centered fragment (H-052) descriptor. Quantitative structure-activity relationship studies demonstrated that the insecticidal activity of these compounds was mainly influenced by many factors, such as electronic distribution, steric factors, etc. For this model, the standard deviation error in prediction (SDEP) is 0.0592, the correlation coefficient (R(2)) is 0.861, and the leave-one-out cross-validation correlation coefficient (Q(2)loo) is 0.797.

  10. Hologram QSAR studies of antiprotozoal activities of sesquiterpene lactones.

    PubMed

    Trossini, Gustavo H G; Maltarollo, Vinícius G; Schmidt, Thomas J

    2014-07-18

    Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs) with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q2 values ranging from 0.637 to 0.775) and external validation coefficients (r2test values ranging from 0.653 to 0.944). From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.

  11. A quantitative structure-activity relationship (QSAR) study on a few series of potent, highly selective inhibitors of nitric oxide synthase.

    PubMed

    Bharti, Vishwa Deepak; Gupta, Satya P; Kumar, Harish

    2014-02-01

    QSAR study was performed on a series of 1,2-dihydro-4-quinazolinamines, 4,5-dialkylsubstituted-2-imino-1,3-thiazolidine derivatives and 4,5-disubstituted-1,3-oxazolidin-2-imine derivatives studied by Tinker et al. [J Med Chem (2003), 46, 913-916], Ueda et al. [Bioorg Med Chem (2004) 12, 4101-4116] and Ueda et al. [Bioorg Med Chem Lett (2004) 14, 313-316], respectively, as potent, highly selective inhibitors of inducible nitric oxide synthase (iNOS). The iNOS inhibition activity of the whole series of compounds was analyzed in relation to the physicochemical and molecular properties of the compounds. The QSAR analysis revealed that the inhibition potency of the compounds was controlled by a topological parameter 1chi(v) (Kier's first order valence molecular connectivity index), density (D), surface tension (St) and length (steric parameter) of a substituent. This suggested that the drug-receptor interaction predominantly involved the dispersion interaction, but the bulky molecule would face steric problem because of which the molecule may not completely fit in active sites of the receptor and thus may not have the optimum interaction.

  12. Interspecies quantitative structure-activity relationships (QSARs) for eco-toxicity screening of chemicals: the role of physicochemical properties.

    PubMed

    Furuhama, A; Hasunuma, K; Aoki, Y

    2015-01-01

    In addition to molecular structure profiles, descriptors based on physicochemical properties are useful for explaining the eco-toxicities of chemicals. In a previous study we reported that a criterion based on the difference between the partition coefficient (log POW) and distribution coefficient (log D) values of chemicals enabled us to identify aromatic amines and phenols for which interspecies relationships with strong correlations could be developed for fish-daphnid and algal-daphnid toxicities. The chemicals that met the log D-based criterion were expected to have similar toxicity mechanisms (related to membrane penetration). Here, we investigated the applicability of log D-based criteria to the eco-toxicity of other kinds of chemicals, including aliphatic compounds. At pH 10, use of a log POW - log D > 0 criterion and omission of outliers resulted in the selection of more than 100 chemicals whose acute fish toxicities or algal growth inhibition toxicities were almost equal to their acute daphnid toxicities. The advantage of log D-based criteria is that they allow for simple, rapid screening and prioritizing of chemicals. However, inorganic molecules and chemicals containing certain structural elements cannot be evaluated, because calculated log D values are unavailable.

  13. 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.

  14. Virulence Factor-activity Relationships: Workshop Summary

    EPA Science Inventory

    The concept or notion of virulence factor–activity relationships (VFAR) is an approach for identifying an analogous process to the use of qualitative structure–activity relationships (QSAR) for identifying new microbial contaminants. In QSAR, it is hypothesized that, for new chem...

  15. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

  16. Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

    One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167

  17. 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...

  18. Anticancer Activity of Estradiol Derivatives: A Quantitative Structure--Activity Relationship Approach

    NASA Astrophysics Data System (ADS)

    Muranaka, Ken

    2001-10-01

    Commercial packages to implement modern QSAR (quantitative structure-activity relationship) techniques are highly priced; however, the essence of QSAR can be taught without them. Microsoft Excel was used to analyze published data on anticancer activities of estradiol analogs by a QSAR approach. The resulting QSAR equations highly correlate the structural features and physicochemical properties of the analogs with the observed biological activities by multiple linear regression.

  19. 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.

  20. 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.

  1. Synthesis, in vitro antitubercular activity and 3D-QSAR study of 1,4-dihydropyridines.

    PubMed

    Manvar, Atul T; Pissurlenkar, Raghuvir R S; Virsodia, Vijay R; Upadhyay, Kuldip D; Manvar, Dinesh R; Mishra, Arun K; Acharya, Hrishikesh D; Parecha, Alpesh R; Dholakia, Chintan D; Shah, Anamik K; Coutinho, Evans C

    2010-05-01

    In continuation of our research program on new antitubercular agents, this article is a report of the synthesis of 97 various symmetrical, unsymmetrical, and N-substituted 1,4-dihydropyridines. The synthesized molecules were tested for their activity against M. tuberculosis H (37)Rv strain with rifampin as the standard drug. The percentage inhibition was found in the range 3-93%. In an effort to understand the relationship between structure and activity, 3D-QSAR studies were also carried out on a subset that is representative of the molecules synthesized. For the generation of the QSAR models, a training set of 35 diverse molecules representing the synthesized molecules was utilized. The molecules were aligned using the atom-fit technique. The CoMFA and CoMSIA models generated on the molecules aligned by the atom-fit method show a correlation coefficient (r (2)) of 0.98 and 0.95 with cross-validated r (2)(q (2)) of 0.56 and 0.62, respectively. The 3D-QSAR models were externally validated against a test set of 19 molecules (aligned previously with the training set) for which the predictive r(2)(r(r)(pred)) is recorded as 0.74 and 0.69 for the CoMFA and CoMSIA models, respectively. The models were checked for chance correlation through y-scrambling. The QSAR models revealed the importance of the conformational flexibility of the substituents in antitubercular activity.

  2. Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

  3. Uncertainty in QSAR predictions.

    PubMed

    Sahlin, Ullrika

    2013-03-01

    It is relevant to consider uncertainty in individual predictions when quantitative structure-activity (or property) relationships (QSARs) are used to support decisions of high societal concern. Successful communication of uncertainty in the integration of QSARs in chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system can be facilitated by a common understanding of how to define, characterise, assess and evaluate uncertainty in QSAR predictions. A QSAR prediction is, compared to experimental estimates, subject to added uncertainty that comes from the use of a model instead of empirically-based estimates. A framework is provided to aid the distinction between different types of uncertainty in a QSAR prediction: quantitative, i.e. for regressions related to the error in a prediction and characterised by a predictive distribution; and qualitative, by expressing our confidence in the model for predicting a particular compound based on a quantitative measure of predictive reliability. It is possible to assess a quantitative (i.e. probabilistic) predictive distribution, given the supervised learning algorithm, the underlying QSAR data, a probability model for uncertainty and a statistical principle for inference. The integration of QSARs into risk assessment may be facilitated by the inclusion of the assessment of predictive error and predictive reliability into the "unambiguous algorithm", as outlined in the second OECD principle.

  4. Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

    PubMed

    Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; van der Wal, Leon; Gramatica, Paola

    2013-08-15

    Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.

  5. 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.

  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. 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.

  8. 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.

  9. 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

  10. A new computer program for QSAR-analysis: ARTE-QSAR.

    PubMed

    Van Damme, Sofie; Bultinck, Patrick

    2007-08-01

    A new computer program has been designed to build and analyze quantitative-structure activity relationship (QSAR) models through regression analysis. The user is provided with a range of regression and validation techniques. The emphasis of the program lies mainly in the validation of QSAR models in chemical applications. ARTE-QSAR produces an easy interpretable output from which the user can conclude if the obtained model is suitable for prediction and analysis.

  11. QSAR and Docking Studies on Capsazepine Derivatives for Immunomodulatory and Anti-Inflammatory Activity

    PubMed Central

    Shukla, Aparna; Sharma, Pooja; Prakash, Om; Singh, Monika; Kalani, Komal; Khan, Feroz; Bawankule, Dnyaneshwar Umrao; Luqman, Suaib; Srivastava, Santosh Kumar

    2014-01-01

    Capsazepine, an antagonist of capsaicin, is discovered by the structure and activity relationship. In previous studies it has been found that capsazepine has potency for immunomodulation and anti-inflammatory activity and emerging as a favourable target in quest for efficacious and safe anti-inflammatory drug. Thus, a 2D quantitative structural activity relationship (QSAR) model against target tumor necrosis factor-α (TNF-α) was developed using multiple linear regression method (MLR) with good internal prediction (r2 = 0.8779) and external prediction (r2pred = 0.5865) using Discovery Studio v3.5 (Accelrys, USA). The predicted activity was further validated by in vitro experiment. Capsazepine was tested in lipopolysaccharide (LPS) induced inflammation in peritoneal mouse macrophages. Anti-inflammatory profile of capsazepine was assessed by its potency to inhibit the production of inflammatory mediator TNF-α. The in vitro experiment indicated that capsazepine is an efficient anti-inflammatory agent. Since, the developed QSAR model showed significant correlations between chemical structure and anti-inflammatory activity, it was successfully applied in the screening of forty-four virtual derivatives of capsazepine, which finally afforded six potent derivatives, CPZ-29, CPZ-30, CPZ-33, CPZ-34, CPZ-35 and CPZ-36. To gain more insights into the molecular mechanism of action of capsazepine and its derivatives, molecular docking and in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were performed. The results of QSAR, molecular docking, in silico ADMET screening and in vitro experimental studies provide guideline and mechanistic scope for the identification of more potent anti-inflammatory & immunomodulatory drug. PMID:25003344

  12. QSAR for cholinesterase inhibition by organophosphorus esters and CNDO/2 calculations for organophosphorus ester hydrolysis. [quantitative structure-activity relationship, complete neglect of differential overlap

    NASA Technical Reports Server (NTRS)

    Johnson, H.; Kenley, R. A.; Rynard, C.; Golub, M. A.

    1985-01-01

    Quantitative structure-activity relationships were derived for acetyl- and butyrylcholinesterase inhibition by various organophosphorus esters. Bimolecular inhibition rate constants correlate well with hydrophobic substituent constants, and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants. CNDO/2 calculations were performed on a separate set of organophosphorus esters, RR-primeP(O)X, where R and R-prime are alkyl and/or alkoxy groups and X is fluorine, chlorine or a phenoxy group. For each subset with the same X, the CNDO-derived net atomic charge at the central phosphorus atom in the ester correlates well with the alkaline hydrolysis rate constant. For the whole set of esters with different X, two equations were derived that relate either charge and leaving group steric bulk, or orbital energy and bond order to the hydrolysis rate constant.

  13. Comparison of Different 2D and 3D-QSAR Methods on Activity Prediction of Histamine H3 Receptor Antagonists.

    PubMed

    Dastmalchi, Siavoush; Hamzeh-Mivehroud, Maryam; Asadpour-Zeynali, Karim

    2012-01-01

    Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error (MAPE), ranging from 2.9 to 3.6, and standard deviation of error of prediction (SDEP), ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses.

  14. 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.

  15. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.

    PubMed

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola

    2006-11-01

    A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

  16. Antibacterial activity and QSAR of chalcones against biofilm-producing bacteria isolated from marine waters.

    PubMed

    Sivakumar, P M; Prabhawathi, V; Doble, M

    2010-04-01

    Biofouling in the marine environment is a major problem. In this study, three marine organisms, namely Bacillus flexus (LD1), Pseudomonas fluorescens (MD3) and Vibrio natriegens (MD6), were isolated from biofilms formed on polymer and metal surfaces immersed in ocean water. Phylogenetic analysis of these three organisms indicated that they were good model systems for studying marine biofouling. The in vitro antifouling activity of 47 synthesized chalcone derivatives was investigated by estimating the minimum inhibitory concentration against these organisms using a twofold dilution technique. Compounds C-5, C-16, C-24, C-33, C-34 and C-37 were found to be the most active. In the majority of the cases it was found that these active compounds had hydroxyl substitutions. A quantitative structure-activity relationship (QSAR) was developed after dividing the total data into training and test sets. The statistical measures r(2), [image omitted] (>0.6) q(2) (>0.5) and the F-ratio were found to be satisfactory. Spatial, structural and electronic descriptors were found to be predominantly affecting the antibiofouling activity of these compounds. Among the spatial descriptors, Jurs descriptors showed their contribution in all the three antibacterial QSARs.

  17. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

    PubMed

    Martin, Eric; Mukherjee, Prasenjit; Sullivan, David; Jansen, Johanna

    2011-08-22

    Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be

  18. Anabolic and androgenic activities of 19-nor-testosterone steroids: QSAR study using quantum and physicochemical molecular descriptors.

    PubMed

    Alvarez-Ginarte, Yoanna María; Montero-Cabrera, Luis Alberto; de la Vega, José Manuel García; Noheda-Marín, Pedro; Marrero-Ponce, Yovani; Ruíz-García, José Alberto

    2011-08-01

    Quantitative structure-activity relationship (QSAR) study of 19-nor-testosterone steroids family was performed using quantum and physicochemical molecular descriptors. The quantum-chemical descriptors were calculated using semiempirical calculations. The descriptor values were statistically correlated using multi-linear regression analysis. The QSAR study indicated that the electronic properties of these derivatives have significant relationship with observed biological activities. The found QSAR equations explain that the energy difference between the LUMO and HOMO, the total dipole moment, the chemical potential and the value of the net charge of different carbon atoms in the steroid nucleus showed key interaction of these steroids with their anabolic-androgenic receptor binding site. The calculated values predict that the 17α-cyclopropyl-17β, 3β-hydroxy-4-estrene compound presents the highest anabolic-androgenic ratio (AAR) and the 7α-methyl-17β-acetoxy-estr-4-en-3-one compound the lowest AAR. This study might be helpful in the future successful identification of "real" or "virtual" anabolic-androgenic steroids.

  19. QSAR in Chromatography: Quantitative Structure-Retention Relationships (QSRRs)

    NASA Astrophysics Data System (ADS)

    Kaliszan, Roman; Bączek, Tomasz

    To predict a given physicochemical or biological property, the relationships can be identified between the chemical structure and the desired property. Ideally these relationships should be described in reliable quantitative terms. To obtain statistically significant relationships, one needs relatively large series of property parameters. Chromatography is a unique method which can provide a great amount of quantitatively precise, reproducible, and comparable retention data for large sets of structurally diversified compounds (analytes). On the other hand, chemometrics is recognized as a valuable tool for accomplishing a variety of tasks in a chromatography laboratory. Chemometrics facilitates the interpretation of large sets of complex chromatographic and structural data. Among various chemometric methods, multiple regression analysis is most often performed to process retention data and to extract chemical information on analytes. And the methodology of quantitative structure-(chromatographic) retention relationships (QSRRs) is mainly based on multiple regression analysis. QSRR can be a valuable source of knowledge on both the nature of analytes and of the macromolecules forming the stationary phases. Therefore, quantitative structure-retention relationships have been considered as a model approach to establish strategy and methods of property predictions.

  20. Probing the origins of aromatase inhibitory activity of disubstituted coumarins via QSAR and molecular docking.

    PubMed

    Worachartcheewan, Apilak; Suvannang, Naravut; Prachayasittikul, Supaluk; Prachayasittikul, Virapong; Nantasenamat, Chanin

    2014-01-01

    This study investigated the quantitative structure-activity relationship (QSAR) of imidazole derivatives of 4,7-disubstituted coumarins as inhibitors of aromatase, a potential therapeutic protein target for the treatment of breast cancer. Herein, a series of 3,7- and 4,7-disubstituted coumarin derivatives (1-34) with R1 and R2 substituents bearing aromatase inhibitory activity were modeled as a function of molecular and quantum chemical descriptors derived from low-energy conformer geometrically optimized at B3LYP/6-31G(d) level of theory. Insights on origins of aromatase inhibitory activity was afforded by the computed set of 7 descriptors comprising of F10[N-O], Inflammat-50, Psychotic-80, H-047, BELe1, B10[C-O] and MAXDP. Such significant descriptors were used for QSAR model construction and results indicated that model 4 afforded the best statistical performance. Good predictive performance were achieved as verified from the internal (comprising the training and the leave-one-out cross-validation (LOO-CV) sets) and external sets affording the following statistical parameters: R (2) Tr = 0.9576 and RMSETr = 0.0958 for the training set; Q (2) CV = 0.9239 and RMSECV = 0.1304 for the LOO-CV set as well as Q (2) Ext = 0.7268 and RMSEExt = 0.2927 for the external set. Significant descriptors showed correlation with functional substituents, particularly, R1 in governing high potency as aromatase inhibitor. Molecular docking calculations suggest that key residues interacting with the coumarins were predominantly lipophilic or non-polar while a few were polar and positively-charged. Findings illuminated herein serve as the impetus that can be used to rationally guide the design of new aromatase inhibitors.

  1. Structure--Antimicrobial Activity Relationship Comparing a New Class of Antimicrobials, Silanols, to Alcohols and Phenols

    DTIC Science & Technology

    2006-04-01

    Hoekman D, Leo A, Zhang LT, Li P, "The Expanding Role of Quantitative Structure–Activity Relationships ( QSAR ) in Toxicology ." Toxicol Lett 1995; 79: 45...partition coefficients (log P) and H-bond acidities (Continued on p. ii) Antimicrobial, Bacteria, Gram-negative, Gram-positive, QSAR , silanol U U U UU...Med Chem 1968; 11: 430–441. [9] Kubinyi H, QSAR : Hansch Analysis and Related Approaches. New York: VCH Publishers, 1993. [10] Kim Y, Farrah S

  2. Synthesis, insecticidal activity, and QSAR of novel nitromethylene neonicotinoids with tetrahydropyridine fixed cis configuration and exo-ring ether modification.

    PubMed

    Tian, Zhongzhen; Shao, Xusheng; Li, Zhong; Qian, Xuhong; Huang, Qingchun

    2007-03-21

    To keep the nitro group in the cis position, a series of nitromethylene neonicotinoids containing a tetrahydropyridine ring with exo-ring ether modifications were designed and synthesized. All of the compounds were characterized and confirmed by 1H NMR, high-resolution mass spectroscopy, elemental analysis, and IR. The bioassay tests showed that some of them exhibited good insecticidal activities against pea aphids. On the basis of 10 nitromethylene derivatives, the quantitative structure-bioactivity relationship (QSAR) was analyzed and established. The results suggested that AlogP98 and Dipole_Mopac might be the important parameters related with biological activities.

  3. QSAR study on the antibacterial activity of some sulfa drugs: building blockers of Mannich bases.

    PubMed

    Mandloi, Dheeraj; Joshi, Sheela; Khadikar, Padmakar V; Khosla, Navita

    2005-01-17

    Sulfa drugs are building blockers of several types of Mannich bases. Consequently, the antibacterial activities of sulfa drugs are reported in this paper, which will help in explaining and understanding antibacterial activities of Mannich bases. Reported QSAR is carried out using distance-based topological indices and discussed critically on the basis of statistical parameters.

  4. 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.

  5. 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

  6. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR CHEMICAL REDUCTIONS OF ORGANIC CONTAMINANTS

    EPA Science Inventory

    Sufficient kinetic data on abiotic reduction reactions involving organic contaminants are now available that quantitative structure-activity relationships (QSARs) for these reactions can be developed. Over 50 QSARs have been reported, most in just the last few years, and they ar...

  7. 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...

  8. QSAR analyses of organophosphates for insecticidal activity and its in-silico validation using molecular docking study.

    PubMed

    Niraj, Ravi Ranjan Kumar; Saini, Vandana; Kumar, Ajit

    2015-11-01

    The present work was carried out to design and develop novel QSAR models using 2D-QSAR and 3D-QSAR with CoMFA methodology for prediction of insecticidal activity of organophosphate (OP) molecules. The models were validated on an entirely different external dataset of in-house generated combinatorial library of OPs, by completely different computational approach of molecular docking against the target AChE protein of Musca domestica. The dock scores were observed to be in good correlation with 2D-QSAR and 3D-QSAR with CoMFA predicted activities and had the correlation coefficients (r(2)) of -0.62 and -0.63, respectively. The activities predicted by 2D-QSAR and 3D-QSAR with CoMFA were also observed to be highly correlated with r(2)=0.82. Also, the combinatorial library molecules were screened for toxicity in non-target organisms and degradability using USEPA-EPI Suite. The work was first step towards computer aided design and development of novel OP pesticide candidates with good insecticidal property but lower toxicity in non-targeted organisms and having biodegradation potential.

  9. QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds.

    PubMed

    Kovarich, S; Papa, E; Li, J; Gramatica, P

    2012-01-01

    Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

  10. 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

  11. QSAR modeling of aromatase inhibitory activity of 1-substituted 1,2,3-triazole analogs of letrozole.

    PubMed

    Nantasenamat, Chanin; Worachartcheewan, Apilak; Prachayasittikul, Supaluk; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2013-11-01

    Aromatase is an estrogen biosynthesis enzyme belonging to the cytochrome P450 family that catalyzes the rate-limiting step of converting androgens to estrogens. As it is pertinent toward tumor cell growth promotion, aromatase is a lucrative therapeutic target for breast cancer. In the pursuit of robust aromatase inhibitors, a set of fifty-four 1-substituted mono- and bis-benzonitrile or phenyl analogs of 1,2,3-triazole letrozole were employed in quantitative structure-activity relationship (QSAR) study using multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). Such QSAR models were developed using a set of descriptors providing coverage of the general characteristics of a molecule encompassing molecular size, flexibility, polarity, solubility, charge and electronic properties. Important physicochemical properties giving rise to good aromatase inhibition were obtained by means of exploring its chemical space as a function of the calculated molecular descriptors. The optimal subset of 3 descriptors (i.e. number of rings, ALogP and HOMO-LUMO) was further used for QSAR model construction. The predicted pIC₅₀ values were in strong correlation with their experimental values displaying correlation coefficient values in the range of 0.72-0.83 for the cross-validated set (QCV) while the external test set (Q(Ext)) afforded values in the range of 0.65-0.66. Insights gained from the present study are anticipated to provide pertinent information contributing to the origins of aromatase inhibitory activity and therefore aid in our on-going quest for aromatase inhibitors with robust properties.

  12. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines.

    PubMed

    Huang, Shan-Han; Tung, Chun-Wei; Fülöp, Ferenc; Li, Jih-Heng

    2015-04-01

    The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM.

  13. 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

  14. Synthesis and QSAR study of novel anti-inflammatory active mesalazine-metronidazole conjugates.

    PubMed

    Naumov, Roman N; Panda, Siva S; Girgis, Adel S; George, Riham F; Farhat, Michel; Katritzky, Alan R

    2015-06-01

    Novel, mesalazine, metronidazole conjugates 6a-e with amino acid linkers were synthesized utilizing benzotriazole chemistry. Biological data acquired for all the novel bis-conjugates showed (a) some bis-conjugates exhibit comparable anti-inflammatory activity with parent drugs and (b) the potent bis-conjugates show no visible stomach lesions. 3D-pharmacophore and 2D-QSAR modeling support the observed bio-properties.

  15. Synthesis, antifungal activity, and QSAR studies of 1,6-dihydropyrimidine derivatives

    PubMed Central

    Rami, Chirag; Patel, Laxmanbhai; Patel, Chhaganbhai N.; Parmar, Jayshree P.

    2013-01-01

    Introduction: A practical synthesis of pyrimidinone would be very helpful for chemists because pyrimidinone is found in many bioactive natural products and exhibits a wide range of biological properties. The biological significance of pyrimidine derivatives has led us to the synthesis of substituted pyrimidine. Materials and Methods: With the aim of developing potential antimicrobials, new series of 5-cyano-6-oxo-1,6-dihydro-pyrimidine derivatives namely 2-(5-cyano-6-oxo-4-substituted (aryl)-1,6-dihydropyrimidin-2-ylthio)-N-substituted (phenyl) acetamide (C1-C41) were synthesized and characterized by Fourier transform infrared spectroscopy (FTIR), mass analysis, and proton nuclear magnetic resonance (1H NMR). All the compounds were screened for their antifungal activity against Candida albicans (MTCC, 227). Results and Discussion: Quantitative structure activity relationship (QSAR) studies of a series of 1,6-dihydro-pyrimidine were carried out to study various structural requirements for fungal inhibition. Various lipophilic, electronic, geometric, and spatial descriptors were correlated with antifungal activity using genetic function approximation. Developed models were found predictive as indicated by their square of predictive regression values (r2pred) and their internal and external cross-validation. Study reveals that CHI_3_C, Molecular_SurfaceArea, and Jurs_DPSA_1 contributed significantly to the activity along with some electronic, geometric, and quantum mechanical descriptors. Conclusion: A careful analysis of the antifungal activity data of synthesized compounds revealed that electron withdrawing substitution on N-phenyl acetamide ring of 1,6-dihydropyrimidine moiety possess good activity. PMID:24302836

  16. 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.

  17. QSAR study on the antinociceptive activity of some morphinans.

    PubMed

    Ramírez-Galicia, Guillermo; Garduño-Juárez, Ramón; Hemmateenejad, Bahram; Deeb, Omar; Deciga-Campos, Myrna; Moctezuma-Eugenio, Juan Carlos

    2007-07-01

    Quantitative structure-activity relationship studies were performed to describe and predict the antinociceptive activity of 31 morphinan derivatives reported by the US Drug Evaluation Committee in 2005 and 2006. From these, three data sets were constructed and several models were calculated following the multiple linear regression and Leave-One-Out Cross-Validation (LOO-CV) tests. In general, these models achieved good descriptive power (approximately 92%) as well as predictive power (approximately 76%), but were unable to predict an external validation set of morphinan derivatives. When artificial neural networks were applied to these models, an improvement of the predictive and external validation values was obtained. It was observed that the results of the NN models are significantly better that those obtained by multiple linear regression. In spite that the problem under investigation can be handled adequately by a linear model, a neural network does bring slight improvements in the predictive power.

  18. QSAR studies of PC-3 cell line inhibition activity of TSA and SAHA-like hydroxamic acids.

    PubMed

    Wang, Di-Fei; Wiest, Olaf; Helquist, Paul; Lan-Hargest, Hsuan-Yin; Wiech, Norbert L

    2004-02-09

    Quantitative structure-activity relationships (QSAR) for a series of new trichostatin A (TSA)-like hydroxamic acids for the inhibition of cell proliferation of the PC-3 cell line have been developed using molecular descriptors from Qikprop and electronic structure calculations. The best regression model shows that the PM3 atomic charge on the carbonyl carbon in the CONHOH moiety(Qco), globularity (Glob), and the hydrophilic component of the solvent-accessible surface area (FISA) describe the IC(50) of 19 inhibitors of the PC-3 cell line with activities ranging over five orders of magnitude with an R(2)=0.92 and F=59.2. This information will be helpful in the further design of novel anticancer drugs for treatment of prostate cancer and other diseases affected by HDAC inhibition.

  19. Prediction and evaluation of the lipase inhibitory activities of tea polyphenols with 3D-QSAR models

    PubMed Central

    Li, Yi-Fang; Chang, Yi-Qun; Deng, Jie; Li, Wei-Xi; Jian, Jie; Gao, Jia-Suo; Wan, Xin; Gao, Hao; Kurihara, Hiroshi; Sun, Ping-Hua; He, Rong-Rong

    2016-01-01

    The extraordinary hypolipidemic effects of polyphenolic compounds from tea have been confirmed in our previous study. To gain compounds with more potent activities, using the conformations of the most active compound revealed by molecular docking, a 3D-QSAR pancreatic lipase inhibitor model with good predictive ability was established and validated by CoMFA and CoMISA methods. With good statistical significance in CoMFA (r2cv = 0.622, r2 = 0.956, F = 261.463, SEE = 0.096) and CoMISA (r2cv = 0.631, r2 = 0.932, F = 75.408, SEE = 0.212) model, we summarized the structure-activity relationship between polyphenolic compounds and pancreatic lipase inhibitory activities and find the bulky substituents in R2, R4 and R5, hydrophilic substituents in R1 and electron withdrawing groups in R2 are the key factors to enhance the lipase inhibitory activities. Under the guidance of the 3D-QSAR results, (2R,3R,2′R,3′R)-desgalloyloolongtheanin-3,3′-O-digallate (DOTD), a potent lipase inhibitor with an IC50 of 0.08 μg/ml, was obtained from EGCG oxidative polymerization catalyzed by crude polyphenol oxidase. Furthermore, DOTD was found to inhibit lipid absorption in olive oil-loaded rats, which was related with inhibiting the activities of lipase in the intestinal mucosa and contents. PMID:27694956

  20. QNA-based 'Star Track' QSAR approach.

    PubMed

    Filimonov, D A; Zakharov, A V; Lagunin, A A; Poroikov, V V

    2009-10-01

    In the existing quantitative structure-activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the 'Star Track' methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. We show that in the majority of cases the accuracy and predictivity of GUSAR models appears to be better than those for the reference QSAR methods. High predictive ability and robustness of GUSAR are also shown in the leave-20%-out cross-validation procedure.

  1. 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds.

    PubMed

    Ragno, Rino; Simeoni, Silvia; Valente, Sergio; Massa, Silvio; Mai, Antonello

    2006-01-01

    Docking simulation and three-dimensional quantitative structure-activity relationships (3D-QSARs) analyses were conducted on four series of HDAC inhibitors. The studies were performed using the GRID/GOLPE combination using structure-based alignment. Twelve 3-D QSAR models were derived and discussed. Compared to previous studies on similar inhibitors, the present 3-D QSAR investigation proved to be of higher statistical value, displaying for the best global model r2, q2, and cross-validated SDEP values of 0.94, 0.83, and 0.41, respectively. A comparison of the 3-D QSAR maps with the structural features of the binding site showed good correlation. The results of 3D-QSAR and docking studies validated each other and provided insight into the structural requirements for anti-HDAC activity. To our knowledge this is the first 3-D QSAR application on a broad molecular diversity training set of HDACIs.

  2. Relationship between antimold activity and molecular structure of cinnamaldehyde analogues.

    PubMed

    Zhang, Yuanyuan; Li, Shujun; Kong, Xianchao

    2013-03-01

    A quantitative structure-activity relationship (QSAR) modeling of the antimold activity of cinnamaldehyde analogues against of Aspergillus niger and Paecilomyces variotii was presented. The molecular descriptors of cinnamaldehyde analogues were calculated by the CODESSA program, and these descriptors were selected by best multi-linear regression method (BMLR). Satisfactory multilinear regression models of Aspergillus niger and Paecilomyces variotii were obtained with R(2)=0.9099 and 0.9444, respectively. The models were also satisfactorily validated using internal validation and leave one out validation. The QSAR models provide the guidance for further synthetic work.

  3. The Role of Qsar Methodology in the Regulatory Assessment of Chemicals

    NASA Astrophysics Data System (ADS)

    Worth, Andrew Paul

    The aim of this chapter is to outline the different ways in which quantitative structure-activity relationship (QSAR) methods can be used in the regulatory assessment of chemicals. The chapter draws on experience gained in the European Union in the assessment of industrial chemicals, as well as recently developed guidance for the use of QSARs within specific legislative frameworks such as REACH and the Water Framework Directive. This chapter reviews the concepts of QSAR validity, applicability, and acceptability and emphasises that the use of individual QSAR estimates is highly context-dependent, which has implications in terms of the confidence needed in the model validity. In addition to the potential use of QSAR models as stand-alone estimation methods, it is expected that QSARs will be used within the context of broader weight-of-evidence approaches, such as chemical categories and integrated testing strategies; therefore, the role of (Q)SARs within these approaches is explained. This chapter also refers to a range of freely available software tools being developed to facilitate the use of QSARs for regulatory purposes. Finally, some conclusions are drawn concerning current needs for the further development and uptake of QSARs

  4. Quantitative structure-activity relationships for fluoroelastomer/chlorofluorocarbon systems

    SciTech Connect

    Paciorek, K.J.L.; Masuda, S.R.; Nakahara, J.H. ); Snyder, C.E. Jr.; Warner, W.M. )

    1991-12-01

    This paper reports on swell, tensile, and modulus data that were determined for a fluoroelastomer after exposure to a series of chlorofluorocarbon model fluids. Quantitative structure-activity relationships (QSAR) were developed for the swell as a function of the number of carbons and chlorines and for tensile strength as a function of carbon number and chlorine positions in the chlorofluorocarbons.

  5. The Use of Qsar and Computational Methods in Drug Design

    NASA Astrophysics Data System (ADS)

    Bajot, Fania

    The application of quantitative structure-activity relationships (QSARs) has significantly impacted the paradigm of drug discovery. Following the successful utilization of linear solvation free-energy relationships (LSERs), numerous 2D- and 3D-QSAR methods have been developed, most of them based on descriptors for hydrophobicity, polarizability, ionic interactions, and hydrogen bonding. QSAR models allow for the calculation of physicochemical properties (e.g., lipophilicity), the prediction of biological activity (or toxicity), as well as the evaluation of absorption, distribution, metabolism, and excretion (ADME). In pharmaceutical research, QSAR has a particular interest in the preclinical stages of drug discovery to replace tedious and costly experimentation, to filter large chemical databases, and to select drug candidates. However, to be part of drug discovery and development strategies, QSARs need to meet different criteria (e.g., sufficient predictivity). This chapter describes the foundation of modern QSAR in drug discovery and presents some current challenges and applications for the discovery and optimization of drug candidates

  6. ESTIMATION OF MICROBIAL REDUCTIVE TRANSFORMATION RATES FOR CHLORINATED BENZENES AND PHENOLS USING A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP APPROACH

    EPA Science Inventory

    A set of literature data was used to derive several quantitative structure-activity relationships (QSARs) to predict the rate constants for the microbial reductive dehalogenation of chlorinated aromatics. Dechlorination rate constants for 25 chloroaromatics were corrected for th...

  7. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-08-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

  8. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

    PubMed Central

    Eriksson, Lennart; Jaworska, Joanna; Worth, Andrew P; Cronin, Mark T D; McDowell, Robert M; Gramatica, Paola

    2003-01-01

    This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. PMID:12896860

  9. In vitro mutagenicity and genotoxicity study of a number of short-chain chlorinated hydrocarbons using the micronucleus test and the alkaline single cell gel electrophoresis technique (Comet assay) in human lymphocytes: a structure-activity relationship (QSAR) analysis of the genotoxic and cytotoxic potential.

    PubMed

    Tafazoli, M; Baeten, A; Geerlings, P; Kirsch-Volders, M

    1998-03-01

    Using the micronucleus (MN) test and the alkaline single cell gel electrophoresis (Comet) assay, potential mutagenicity (MN formation), genotoxicity (DNA breakage capacity) and cytotoxicity (cell proliferation reduction) of five chlorinated hydrocarbons (carbon tetrachloride, hexachloroethane, 1,2-dichloroethane, 1-chlorohexane and 2,3-dichlorobutane) have been evaluated in isolated human lymphocytes. With the MN test a low but statistically significant mutagenic activity was detected for all tested substances (except 2,3-dichlorobutane) with one out of the two donors and in the presence or absence of an exogenous metabolic activation system (S9 mix). However, at the concentration ranges tested none of the positive compounds induced a clear dose-dependent mutagenic effect. The Comet assay detected a strong DNA damaging effect for 1-chlorohexane, 2,3-dichlorobutane and 1,2-dichloroethane, but not for carbon tetrachloride and hexachloroethane. The influence of metabolism on the genotoxic activity of the chemicals was more clear in the Comet assay than in the MN test. The experimental genotoxicity and cytotoxicity data obtained in this study, together with data on five more related chemicals previously investigated, and their physico-chemical descriptors or electronic parameters have been used for QSAR analysis. The QSAR analysis high-lighted that the toxicity of the tested compounds was influenced by different parameters, like lipophilicity (logP), electron donor ability (charge) and longest carbon-chlorine (LBC-Cl) bond length. In addition, steric parameters, like molar refractivity (MR) and LBC-Cl, and electronic parameters, like ELUMO (energy of the lowest unoccupied molecular orbital, indicating electrophilicity), were predominant factors discriminating genotoxins from non-genotoxins in the presence but not in the absence of S9 mix. Although a limited number of compounds have been examined and cytotoxicity and genotoxicity were identified in two different

  10. Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls.

    PubMed

    Liu, S; Luo, Y; Fu, J; Zhou, J; Kyzas, G Z

    2016-01-01

    The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR.

  11. Using SAR and QSAR analysis to model the activity and structure of the quinolone-DNA complex.

    PubMed

    Llorente, B; Leclerc, F; Cedergren, R

    1996-01-01

    A set of 78 quinolone derivatives were used in a structure-activity study to identify structural features correlating with antibacterial activity. Distinct combinations of functional properties were identified for Gram-negative and Gram-positive bacteria. 3-D Quantitative structure-activity relationship (QSAR) studies identified specific hydrophobic, topologic and electronic properties of the molecules for both in vitro and in vivo activities. From these results, a three-dimensional model of a DNA-quinolone complex was built using molecular modeling techniques. It was based on the intercalation of quinolone into the double helix of DNA. We conclude that the intercalation model is consistent with most available data on the structure of the quinolone complex. This predicted structure is stabilized by the binding of magnesium ion with the sp2 oxygens present in quinolone, a phosphate and a purine base of the DNA. Substituents R1 and R7 are predicted to make hydrophobic interactions in the major and minor groove of DNA, respectively. R7 could also form hydrogen bonds with amino groups of guanines and the aspartic acid residue at position 87 in DNA gyrase subunit A.

  12. Quantitative structure-activity relationships of selective antagonists of glucagon receptor using QuaSAR descriptors.

    PubMed

    Manoj Kumar, Palanivelu; Karthikeyan, Chandrabose; Hari Narayana Moorthy, Narayana Subbiah; Trivedi, Piyush

    2006-11-01

    In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition.

  13. 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.

  14. 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

  15. Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb.) K. Schum: Isolation, Synthesis and QSAR Study.

    PubMed

    Abdullah, Nor Hayati; Thomas, Noel Francis; Sivasothy, Yasodha; Lee, Vannajan Sanghiran; Liew, Sook Yee; Noorbatcha, Ibrahim Ali; Awang, Khalijah

    2016-02-14

    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.

  16. QSAR trout toxicity models on aromatic pesticides.

    PubMed

    Slavov, Svetoslav; Gini, Giuseppina; Benfenati, Emilio

    2008-11-01

    The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC50 concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.

  17. Antimycobacterial activity evaluation, time-kill kinetic and 3D-QSAR study of C-(3-aminomethyl-cyclohexyl)-methylamine derivatives.

    PubMed

    Kumar, Deepak; Raj, K Kranthi; Bailey, MaiAnn; Alling, Torey; Parish, Tanya; Rawat, Diwan S

    2013-03-01

    A series of C-(3-aminomethyl-cyclohexyl)-methylamine derivatives were synthesized and evaluated for their antitubercular activity. Some of the compounds exhibited potent activity against Mycobacterium tuberculosis H37Rv. One of the compound having t-butyl at para position of the benzene ring showed excellent activity even better than the standard drug ethambutol with MIC value 1.1 ± 0.2 μM. The time-kill kinetics study of two most active compounds showed rapid killing of the M. tuberculosis within 4 days. Additionally atom-based quantitative structure-activity relationship (QSAR) model was developed that gave a statistically satisfying result (R(2))=0.92, Q(2)=0.75, Pearson-R=0.96 and effectively predicts the anti-tuberculosis activity of training and test set compounds.

  18. Characterization of acetylcholinesterase from elm left beetle, Xanthogaleruca luteola and QSAR of temephos derivatives against its activity.

    PubMed

    Sharifi, Mahboobeh; Ghadamyari, Mohammad; Gholivand, Khodayar; Valmoozi, Ali Asghar Ebrahimi; Sajedi, Reza H

    2017-03-01

    Insect acetylcholinesterase (AChE) is the principal target for organophosphate (OP) and carbamate (CB) insecticides. In this research, an AChE from third instar larvae of elm left beetle, Xanthogaleruca luteola was purified by affinity chromatography. The enzyme was purified 75.29-fold with a total yield of 8.51%. As shown on denaturing SDS-PAGE, the molecular mass of purified AChE was 70kDa. The enzyme demonstrated maximum activity at pH7 and 35°C. Furthermore, a series of temephos (Tem) derivatives with the general structure of P(O)XP(O) (1-44) were prepared, synthesized and characterized by (31)P, (13)C, (1)H NMR and FT-IR spectral techniques. The toxicity of 36 new Tem derivatives was screened on the third instar larvae and the compound compound 1,2 cyclohexane-N,N'-bis(N,N'-piperidine phosphoramidate) exhibited the highest insecticidal potential. The method of kinetic analysis is applied in order to obtain the maximum velocity (Vmax), the Michaelis constant (Km) and the parameters characterizing the inhibition type for inhibitors with >75% mortality in preliminary bioassay. The inhibition mechanism was mixed and inhibitory constant (Ki) was calculated as 4.70μM(-1)min(-1) for this compound. Quantitative structure-activity relationship (QSAR) equations of these compounds indicated that the electron orbital energy has major effect on insecticidal properties.

  19. Pharmacophore generation, atom-based 3D-QSAR, HQSAR and activity cliff analyses of benzothiazine and deazaxanthine derivatives as dual A2A antagonists/MAO‑B inhibitors.

    PubMed

    Bhayye, S S; Roy, K; Saha, A

    2016-02-12

    Dual inhibition of A2A and MAO-B is an emerging strategy in neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD). In this study, atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR) and hologram quantitative structure-activity relationship (HQSAR) models were generated with benzothiazine and deazaxanthine derivatives. Based on activity against A2A and MAO-B, two statistically significant 3D-QSAR models (r(2) = 0.96, q(2) = 0.76 and r(2) = 0.91, q(2) = 0.63) and HQSAR models (r(2) = 0.93, q(2) = 0.68 and r(2) = 0.97, q(2) = 0.58) were developed. In an activity cliff analysis, structural outliers were identified by calculating the Mahalanobis distance for a pair of compounds with A2A and MAO-B inhibitory activities. The generated 3D-QSAR and HQSAR models, activity cliff analysis, molecular docking and dynamic studies for dual target protein inhibitors provide key structural scaffolds that serve as building blocks in designing drug-like molecules for neurodegenerative diseases.

  20. Quantitative structure-activity relationships of imidazole-containing farnesyltransferase inhibitors using different chemometric methods.

    PubMed

    Shayanfar, Ali; Ghasemi, Saeed; Soltani, Somaieh; Asadpour-Zeynali, Karim; Doerksen, Robert J; Jouyban, Abolghasem

    2013-05-01

    Farnesyltranseferase inhibitors (FTIs) are one of the most promising classes of anticancer agents, but though some compounds in this category are in clinical trials there are no marketed drugs in this class yet. Quantitative structure activity relationship (QSAR) models can be used for predicting the activity of FTI candidates in early stages of drug discovery. In this study 192 imidazole-containing FTIs were obtained from the literature, structures of the molecules were optimized using Hyperchem software, and molecular descriptors were calculated using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares (GA-PLS) and stepwise regression, and indicated that the volume, shape and polarity of the FTIs are important for their activities. 2D-QSAR models were prepared using both linear methods, i.e., multiple linear regression (MLR), and non-linear methods, i.e., artificial neural networks (ANN) and support vector machines (SVM). The proposed QSAR models were validated using internal and external validation methods. The results show that the proposed 2D-QSAR models are valid and that they can be applied to predict the activities of imidazole-containing FTIs. The prediction capability of the 2D-QSAR (linear and non-linear) models is comparable to and somewhat better than that of previous 3D-QSAR models and the non-linear models are more accurate than the linear models.

  1. Evaluation and QSAR modeling on multiple endpoints of estrogen activity based on different bioassays.

    PubMed

    Liu, Huanxiang; Papa, Ester; Gramatica, Paola

    2008-02-01

    There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, due to the large number of chemicals that have serious adverse effects on the environment. Many approaches using a variety of biological screening assays are used to identify endocrine disrupting chemicals. The present investigation analyzes the consistency and peculiarity of information from different experimental assays collected from a literature survey, by studying the correlation of the different endpoints. In addition, the activity values of more widely used selected bioassays have been combined by principle components analysis (PCA) to build one cumulative endpoint, the estrogen activity index (EAI), for priority setting to identify chemicals most likely possessing estrogen activity for early entry into screening. This index was then modeled using only a few theoretical molecular descriptors. The constructed MLR-QSAR model has been statistically validated for its predictive power, and can be proposed as a preliminary evaluative method to screen/prioritize estrogens according to their integrated estrogen activity, just starting from molecular structure.

  2. Development of Novel Repellents Using Structure - Activity Modeling of Compounds in the USDA Archival Database

    DTIC Science & Technology

    2011-01-01

    used in efforts to develop QSAR models. Measurement of Repellent Efficacy Screening for Repellency of Compounds with Unknown Toxicology In screening...CPT) were used to develop Quantitative Structure Activity Relationship ( QSAR ) models to predict repellency. Successful prediction of novel...acylpiperidine QSAR models employed 4 descriptors to describe the relationship between structure and repellent duration. The ANN model of the carboxamides did not

  3. Quantitative structure-activity relationship modeling of the toxicity of organothiophosphate pesticides to Daphnia magna and Cyprinus carpio.

    PubMed

    Zvinavashe, Elton; Du, Tingting; Griff, Tamas; van den Berg, Hans H J; Soffers, Ans E M F; Vervoort, Jacques; Murk, Albertinka J; Rietjens, Ivonne M C M

    2009-06-01

    Within the REACH regulatory framework in the EU, quantitative structure-activity relationships (QSAR) models are expected to help reduce the number of animals used for experimental testing. The objective of this study was to develop QSAR models to describe the acute toxicity of organothiophosphate pesticides to aquatic organisms. Literature data sets for acute toxicity data of organothiophosphates to fish and one data set from experiments with 15 organothiophosphates on Daphniamagna performed in the present study were used to establish QSARs based on quantum mechanically derived molecular descriptors. The logarithm of the octanol/water partition coefficient, logK(ow,) the energy of the lowest unoccupied molecular orbital, E(lumo), and the energy of the highest occupied molecular orbital, E(homo) were used as descriptors. Additionally, it was investigated if toxicity data for the invertebrate D. magna could be used to build a QSAR model to predict toxicity to fish. Suitable QSAR models (0.80QSAR model to predict the acute toxicity of organothiophosphates to fish. The three QSAR models were validated either both internally and externally (D. magna) or internally only (carp and D. magna to carp). For each QSAR model, an applicability domain was defined based on the chemical structures and the ranges of the descriptor values of the training set compounds. From the 100196 European Inventory of Existing Commercial Chemical Substances (EINECS), 83 compounds were identified that fit the selection criteria for the QSAR models. For these compounds, using our QSAR models, one can obtain an indication of their toxicity without the need for additional experimental

  4. Hierarchical QSAR technology based on the Simplex representation of molecular structure

    NASA Astrophysics Data System (ADS)

    Kuz'min, V. E.; Artemenko, A. G.; Muratov, E. N.

    2008-06-01

    This article is about the hierarchical quantitative structure-activity relationship technology (HiT QSAR) based on the Simplex representation of molecular structure (SiRMS) and its application for different QSAR/QSP(property)R tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) to the QSAR problem by the series of enhanced models of molecular structure description [from one dimensional (1D) to four dimensional (4D)]. It is a system of permanently improved solutions. In the SiRMS approach, every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The level of simplex descriptors detailing increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach reported here are the absence of "molecular alignment" problems, consideration of different physical-chemical properties of atoms (e.g. charge, lipophilicity, etc.), the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of the HiT QSAR approach is demonstrated by comparing it with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the base of directed drug design was validated by subsequent synthetic and biological experiments, among others. The HiT QSAR is realized as a complex of computer programs known as HiT QSAR software that also includes a powerful statistical block and a number of useful utilities.

  5. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?

    PubMed

    Toropova, Alla P; Toropov, Andrey A

    2017-02-11

    New criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs) is suggested. This criterion is calculated with utilization of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The utilization of this criterion improves the predictive potential of QSAR models of dermal permeability coefficient, logKp (cm/h).

  6. Developments in Quantitative Structure-Activity Relationships (QSAR). A Review

    DTIC Science & Technology

    1976-07-01

    hyphae Analogs Inhibition of s-Nitrostyrenes 20 84 Growth Botrytie -,inerea Inhibition of a-Nitrostyrenes 6 84 Grcwth Bovine hemoglobin Binding of...AspergiL us niger, phenyl methacrylates upon Ranse-nula awmat~a and RR’NCSS Na+ upon Botrytis cinerea conformed to the general equation 35. The equations...log II vs log kw *79 Botrytis cinerea , 41, 64 -lg! slgý,7 Bovine hemoglobin, 36 lg Elv o .,7 Bovine serum albumin, 36 - log iI vs log P, 79 - log JE

  7. 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...

  8. Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.

    PubMed

    Saavedra, Laura M; Ruiz, Diego; Romanelli, Gustavo P; Duchowicz, Pablo R

    2015-12-01

    Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii.

  9. 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.

  10. 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.

  11. Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators.

    PubMed

    García-Jacas, C R; Martinez-Mayorga, K; Marrero-Ponce, Y; Medina-Franco, J L

    2017-01-01

    Epigenetic drug discovery is a promising research field with growing interest in the scientific community, as evidenced by the number of publications and the large amount of structure-epigenetic activity information currently available in the public domain. Computational methods are valuable tools to analyse and understand the activity of large compound collections from their structural information. In this manuscript, QSAR models to predict the inhibitory activity of a diverse and heterogeneous set of 88 organic molecules against the bromodomains BRD2, BRD3 and BRD4 are presented. A conformation-dependent representation of the chemical structures was established using the RDKit software and a training and test set division was performed. Several two-linear and three-linear QuBiLS-MIDAS molecular descriptors ( www.tomocomd.com ) were computed to extract the geometric structural features of the compounds studied. QuBiLS-MIDAS-based features sets, to be used in the modelling, were selected using dimensionality reduction strategies. The multiple linear regression procedure coupled with a genetic algorithm were employed to build the predictive models. Regression models containing between 6 to 9 variables were developed and assessed according to several internal and external validation methods. Analyses of outlier compounds and the applicability domain for each model were performed. As a result, the models against BRD2 and BRD3 with 8 variables and the model with 9 variables against BRD4 were those with the best overall performance according to the criteria accounted for. The results obtained suggest that the models proposed will be a good tool for studying the inhibitory activities of drug candidates against the bromodomains considered during epigenetic drug discovery.

  12. Quantitative structure-activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods.

    PubMed

    Xu, A; Zhang, Y; Ran, T; Liu, H; Lu, S; Xu, J; Xiong, X; Jiang, Y; Lu, T; Chen, Y

    2015-01-01

    Bruton's tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure-activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q(2) = 0.884, r(2) = 0.929, r(2)pred = 0.878; 3D-QSAR: q(2) = 0.616, r(2) = 0.987, r(2)pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure-activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.

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

    SciTech Connect

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

    2015-04-15

    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 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 skin

  14. Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses.

    PubMed

    Beck, Jeremy M; Springer, Clayton

    2014-04-28

    The concepts of activity cliffs and matched molecular pairs (MMP) are recent paradigms for analysis of data sets to identify structural changes that may be used to modify the potency of lead molecules in drug discovery projects. Analysis of MMPs was recently demonstrated as a feasible technique for quantitative structure-activity relationship (QSAR) modeling of prospective compounds. Although within a small data set, the lack of matched pairs, and the lack of knowledge about specific chemical transformations limit prospective applications. Here we present an alternative technique that determines pairwise descriptors for each matched pair and then uses a QSAR model to estimate the activity change associated with a chemical transformation. The descriptors effectively group similar transformations and incorporate information about the transformation and its local environment. Use of a transformation QSAR model allows one to estimate the activity change for novel transformations and therefore returns predictions for a larger fraction of test set compounds. Application of the proposed methodology to four public data sets results in increased model performance over a benchmark random forest and direct application of chemical transformations using QSAR-by-matched molecular pairs analysis (QSAR-by-MMPA).

  15. Robust cross-validation of linear regression QSAR models.

    PubMed

    Konovalov, Dmitry A; Llewellyn, Lyndon E; Vander Heyden, Yvan; Coomans, Danny

    2008-10-01

    A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.

  16. 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

  17. Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities.

    PubMed

    Sheridan, Robert P

    2016-11-28

    Several papers have appeared in which a ligand efficiency index instead of pIC50 is used as the activity in QSAR. The claim is that better fits and predictions are obtained with ligand efficiency. We show on both public-domain and in-house data sets that the apparent superiority is a statistical artifact that occurs when ligand efficiency indices are correlated with the physical property included in their definition (number of non-hydrogens, ALOGP, TPSA, etc.) and when the property is easier to predict than the original pIC50.

  18. Atomic softness-based QSAR study of testosterone

    NASA Astrophysics Data System (ADS)

    Srivastava, H. K.; Pasha, F. A.; Singh, P. P.

    Ionization potential of an atom in a molecule, electron affinity of an atom in a molecule, and quantum chemical descriptor atomic softness values En‡-based quantitative structure-activity relationship (QSAR) study of testosterone derivatives have been done with the help of PM3 calculations on WinMOPAC 7.21 software. The 3D modeling and geometry optimization of all the compounds have been done with the help of PCMODEL software. The biological activities of testosterone derivatives have been taken from literature. The predicted values of biological activity with the help of multiple linear regression (MLR) analysis is very close to observed biological activity. The cross-validation coefficient and correlation coefficient also indicate that the QSAR model is valuable. Regression analysis shows a very good relationship with biological activity and En‡ values. With the help of these values, prediction of the biological activity of any unknown compound is possible.

  19. 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

  20. Structure -activity relationships of PDE5 inhibitors.

    PubMed

    Eros, D; Szántai-Kis, Cs; Kiss, R; Kéri, Gy; Hegymegi-Barakonyi, B; Kövesdi, I; Orfi, L

    2008-01-01

    cGMP has a short-term effect on smooth muscle tone and a longer-term effect on responses to chronic drug treatment or proliferative signals. cGMP-Phosphodiesterase type 5 (PDE5) hydrolizes cGMP, and the result is smooth muscle contraction. PDE5 is a relatively novel therapeutic target of various diseases, such as erectile dysfunction and pulmonary hypertension. The most intensively examined and marketed PDE5 inhibitor was sildenafil (Viagra) but recently vardenafil (Levitra) and tadalafil (Cialis) were launched with beneficial ADME parameters and PDE5 selectivity. The increasing interest in PDE5 inhibition made it reasonable to collect the available inhibitory data from the scientific literature and set up a structure-activity relationship study. Chemical structures of 438 compounds and their cGMP-PDE5 inhibitory data (IC50) were collected from recently published articles. In this paper physiology, regulation and inhibition of PDE5 (and briefly other PDE-s) are discussed and inhibitors are tabulated by the core structures. Finally, a general QSAR model built from these data is presented. All data used in the QSAR study were summarized in a Supplement (for description please see the online version of the article).

  1. Prediction oriented QSAR modelling of EGFR inhibition.

    PubMed

    Szántai-Kis, C; Kövesdi, I; Eros, D; Bánhegyi, P; Ullrich, A; Kéri, G; Orfi, L

    2006-01-01

    Epidermal Growth Factor Receptor (EGFR) is a high priority target in anticancer drug research. Thousands of very effective EGFR inhibitors have been developed in the last decade. The known inhibitors are originated from a very diverse chemical space but--without exception--all of them act at the Adenosine TriPhosphate (ATP) binding site of the enzyme. We have collected all of the diverse inhibitor structures and the relevant biological data obtained from comparable assays and built prediction oriented Quantitative Structure-Activity Relationship (QSAR) which models the ATP binding pocket's interactive surface from the ligand side. We describe a QSAR method with automatic Variable Subset Selection (VSS) by Genetic Algorithm (GA) and goodness-of-prediction driven QSAR model building, resulting an externally validated EGFR inhibitory model built from pIC50 values of a diverse structural set of 623 EGFR inhibitors. Repeated Trainings/Evaluations (RTE) were used to obtain model fitness values and the effectiveness of VSS is amplified by using predictive ability scores of descriptors. Numerous models were generated by different methods and viable models were collected. Then, intensive RTE were applied to identify ultimate models for external validations. Finally, suitable models were validated by statistical tests. Since we use calculated molecular descriptors in the modeling, these models are suitable for virtual screening for obtaining novel potential EGFR inhibitors.

  2. Antiproliferative Pt(IV) complexes: synthesis, biological activity, and quantitative structure-activity relationship modeling.

    PubMed

    Gramatica, Paola; Papa, Ester; Luini, Mara; Monti, Elena; Gariboldi, Marzia B; Ravera, Mauro; Gabano, Elisabetta; Gaviglio, Luca; Osella, Domenico

    2010-09-01

    Several Pt(IV) complexes of the general formula [Pt(L)2(L')2(L'')2] [axial ligands L are Cl-, RCOO-, or OH-; equatorial ligands L' are two am(m)ine or one diamine; and equatorial ligands L'' are Cl- or glycolato] were rationally designed and synthesized in the attempt to develop a predictive quantitative structure-activity relationship (QSAR) model. Numerous theoretical molecular descriptors were used alongside physicochemical data (i.e., reduction peak potential, Ep, and partition coefficient, log Po/w) to obtain a validated QSAR between in vitro cytotoxicity (half maximal inhibitory concentrations, IC50, on A2780 ovarian and HCT116 colon carcinoma cell lines) and some features of Pt(IV) complexes. In the resulting best models, a lipophilic descriptor (log Po/w or the number of secondary sp3 carbon atoms) plus an electronic descriptor (Ep, the number of oxygen atoms, or the topological polar surface area expressed as the N,O polar contribution) is necessary for modeling, supporting the general finding that the biological behavior of Pt(IV) complexes can be rationalized on the basis of their cellular uptake, the Pt(IV)-->Pt(II) reduction, and the structure of the corresponding Pt(II) metabolites. Novel compounds were synthesized on the basis of their predicted cytotoxicity in the preliminary QSAR model, and were experimentally tested. A final QSAR model, based solely on theoretical molecular descriptors to ensure its general applicability, is proposed.

  3. 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.

  4. Development of biologically active compounds by combining 3D QSAR and structure-based design methods

    NASA Astrophysics Data System (ADS)

    Sippl, Wolfgang

    2002-11-01

    One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved - namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data

  5. Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives.

    PubMed

    Shibi, I G; Aswathy, L; Jisha, R S; Masand, V H; Divyachandran, A; Gajbhiye, J M

    2015-09-18

    The quinoline moiety is one of the widely studied scaffolds for generating derivatives with various pharmacophoric groups due to its potential antimalarial activities. In the present study, a series of 7-substituted-4-aminoquinoline derivatives were selected to understand their antimalarial properties computationally by molecular modeling techniques including 2D QSAR, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and molecular docking. The 2D-QSAR model built with four descriptors selected by genetic algorithm technique and CoMFA model showed satisfactory statistical results (Q(2)=0.540, R(2)ncv=0.881, F value=157.09). A reliable CoMSIA model out of the fourteen different combinations has a Q(2) value of 0.638. The molecular docking studies of the compounds for 1CET as the protein target revealed that ten compounds showed maximum interactions with the binding site of the protein. The present study highlights the unique binding signatures of the ligands within the active site groove of the target and it explains the subtle differences in their EC50 values and their mechanism of inhibition.

  6. Quantitative structure-antifungal activity relationships of some benzohydrazides against Botrytis cinerea.

    PubMed

    Reino, José L; Saiz-Urra, Liane; Hernandez-Galan, Rosario; Aran, Vicente J; Hitchcock, Peter B; Hanson, James R; Gonzalez, Maykel Perez; Collado, Isidro G

    2007-06-27

    Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.

  7. In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study.

    PubMed

    Gooch, Aminah; Sizochenko, Natalia; Rasulev, Bakhtiyor; Gorb, Leonid; Leszczynski, Jerzy

    2017-02-07

    The toxicity data of 90 nitroaromatic compounds related to their 50% lethal dose concentration for rats (LD50) were analyzed to develop quantitative structure-activity relationship (QSAR) models. Quantum-chemically calculated descriptors together with molecular descriptors generated by DRAGON, PaDEL, and HiT-QSAR software were utilized to build QSAR models. Quality and validity of the models were determined by internal and external validation techniques. The results show that the toxicity of nitroaromatic compounds depends on various factors, such as the number of nitro-groups, the topological state, and the presence of certain structural fragments. The developed models based on the largest (to date) dataset of nitroaromatics in vivo toxicity showed a good predictive ability. The results provide important input that could be applied in a preliminary assessment of nitroaromatic compounds' toxicity to mammals. Environ Toxicol Chem 2017;9999:1-7. © 2017 SETAC.

  8. Modeling the nucleophilic reactivity of small organochlorine electrophiles: A mechanistically based quantitative structure-activity relationship

    SciTech Connect

    Verhaar, H.J.M.; Seinen, W.; Hermens, J.L.M.; Rorije, E.; Borkent, H.

    1996-06-01

    Environmental pollutants can be divided into four broad categories, narcosis-type chemicals, less inert (polar narcosis) chemicals, reactive chemicals, and specifically acting chemicals. For narcosis-type, or baseline, chemicals and for less inert chemicals, adequate quantitative structure-activity relationships (QSARs) are available for estimation of toxicity to aquatic species. This is not the case for reactive chemicals and specifically acting chemicals. A possible approach to develop aquatic toxicity QSARs for reactive chemicals based on simple considerations regarding their reactivity is given. It is shown that quantum chemical calculations on reaction transition states can be used to quantitatively predict the reactivity of sets of reactive chemicals. These predictions can then be used to develop aquatic toxicity QSARs.

  9. Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor.

    PubMed

    Piir, G; Sild, S; Maran, U

    2013-01-01

    Quantitative structure-activity relationships (QSARs) are broadly classified as global or local, depending on their molecular constitution. Global models use large and diverse training sets covering a wide range of chemical space. Local models focus on smaller structurally or chemically similar subsets that are conventionally selected by human experts or alternatively using clustering analysis. The current study focuses on the comparative analysis of different clustering algorithms (expectation-maximization, K-means and hierarchical) for seven different descriptor sets as structural characteristics and two rule-based approaches to select subsets for designing local QSAR models. A total of 111 local QSAR models are developed for predicting bioconcentration factor. Predictions from local models were compared with corresponding predictions from the global model. The comparison of coefficients of determination (r(2)) and standard deviations for local models with similar subsets from the global model show improved prediction quality in 97% of cases. The descriptor content of derived QSARs is discussed and analyzed. Local QSAR models were further consolidated within the framework of consensus approach. All different consensus approaches increased performance over the global and local models. The consensus approach reduced the number of strongly deviating predictions by evening out prediction errors, which were produced by some local QSARs.

  10. 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.

  11. Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase.

    PubMed

    Ruark, Christopher D; Hack, C Eric; Robinson, Peter J; Anderson, Paul E; Gearhart, Jeffery M

    2013-02-01

    Organophosphates are a group of pesticides and chemical warfare nerve agents that inhibit acetylcholinesterase, the enzyme responsible for hydrolysis of the excitatory neurotransmitter acetylcholine. Numerous structural variants exist for this chemical class, and data regarding their toxicity can be difficult to obtain in a timely fashion. At the same time, their use as pesticides and military weapons is widespread, which presents a major concern and challenge in evaluating human toxicity. To address this concern, a quantitative structure-activity relationship (QSAR) was developed to predict pentavalent organophosphate oxon human acetylcholinesterase bimolecular rate constants. A database of 278 three-dimensional structures and their bimolecular rates was developed from 15 peer-reviewed publications. A database of simplified molecular input line entry notations and their respective acetylcholinesterase bimolecular rate constants are listed in Supplementary Material, Table I. The database was quite diverse, spanning 7 log units of activity. In order to describe their structure, 675 molecular descriptors were calculated using AMPAC 8.0 and CODESSA 2.7.10. Orthogonal projection to latent structures regression, bootstrap leave-random-many-out cross-validation and y-randomization were used to develop an externally validated consensus QSAR model. The domain of applicability was assessed by the William's plot. Six external compounds were outside the warning leverage indicating potential model extrapolation. A number of compounds had residuals >2 or <-2, indicating potential outliers or activity cliffs. The results show that the HOMO-LUMO energy gap contributed most significantly to the binding affinity. A mean training R (2) of 0.80, a mean test set R (2) of 0.76 and a consensus external test set R (2) of 0.66 were achieved using the QSAR. The training and external test set RMSE values were found to be 0.76 and 0.88. The results suggest that this QSAR model can be used in

  12. SAR/QSAR methods in public health practice.

    PubMed

    Demchuk, Eugene; Ruiz, Patricia; Chou, Selene; Fowler, Bruce A

    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 protect 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.

  13. Quantitative structure activity relationship studies of mushroom tyrosinase inhibitors

    NASA Astrophysics Data System (ADS)

    Xue, Chao-Bin; Luo, Wan-Chun; Ding, Qi; Liu, Shou-Zhu; Gao, Xing-Xiang

    2008-05-01

    Here, we report our results from quantitative structure-activity relationship studies on tyrosinase inhibitors. Interactions between benzoic acid derivatives and tyrosinase active sites were also studied using a molecular docking method. These studies indicated that one possible mechanism for the interaction between benzoic acid derivatives and the tyrosinase active site is the formation of a hydrogen-bond between the hydroxyl (aOH) and carbonyl oxygen atoms of Tyr98, which stabilized the position of Tyr98 and prevented Tyr98 from participating in the interaction between tyrosinase and ORF378. Tyrosinase, also known as phenoloxidase, is a key enzyme in animals, plants and insects that is responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. In the present study, the bioactivities of 48 derivatives of benzaldehyde, benzoic acid, and cinnamic acid compounds were used to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field (CoMFA) and comparative molecular similarity indices (CoMSIA) analyses. After superimposition using common substructure-based alignments, robust and predictive 3D-QSAR models were obtained from CoMFA ( q 2 = 0.855, r 2 = 0.978) and CoMSIA ( q 2 = 0.841, r 2 = 0.946), with 6 optimum components. Chemical descriptors, including electronic (Hammett σ), hydrophobic (π), and steric (MR) parameters, hydrogen bond acceptor (H-acc), and indicator variable ( I), were used to construct a 2D-QSAR model. The results of this QSAR indicated that π, MR, and H-acc account for 34.9, 31.6, and 26.7% of the calculated biological variance, respectively. The molecular interactions between ligand and target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the benzoic acid derivatives and the tyrosinase active site was elucidated in detail. We believe

  14. Synthesis, antiviral activity, 3D-QSAR, and interaction mechanisms study of novel malonate derivatives containing quinazolin-4(3H)-one moiety.

    PubMed

    Chen, Meihang; Li, Pei; Hu, Deyu; Zeng, Song; Li, Tianxian; Jin, Linhong; Xue, Wei; Song, Baoan

    2016-01-01

    A series of novel malonate derivatives containing quinazolin-4(3H)-one moiety were synthesized and evaluated for their antiviral activities against cucumber mosaic virus (CMV). Results indicated that the title compounds exhibited good antiviral activities. Notably, compounds g15, g16, g17, and g18 exhibited excellent curative activities in vivo against CMV, with 50% effective concentration (EC50) values of 208.36, 153.78, 181.47, and 164.72μg/mL, respectively, which were better than that of Ningnanmycin (256.35μg/mL) and Ribavirin (523.34μg/mL). Moreover, statistically valid three-dimensional quantitative structure-activity relationship (3D-QSAR) models with good correlation and predictive power were obtained with comparative molecular field analysis (CoMFA) steric and electrostatic fields (r(2)=0.990, q(2)=0.577) and comparative molecular similarity indices analysis (CoMSIA) with combined steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (r(2)=0.977, q(2)=0.516), respectively. Based on those models, compound g25 was designed, synthesized, and showed better curative activity (146.30μg/mL) than that of compound g16. The interaction of between cucumber mosaic virus coat protein (CMV CP) and g25 with 1:1.83 ratio is typically spontaneous and exothermic with micromole binding affinity by isothermal titration calorimetry (ITC) and fluorescence spectroscopy investigation.

  15. Partitioning and lipophilicity in quantitative structure-activity relationships.

    PubMed Central

    Dearden, J C

    1985-01-01

    The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structure-activity relationships is emphasized, although the importance of other factors is also demonstrated. Various mathematical models of in vivo transport and binding are discussed; most of these involve partitioning as the primary mechanism of transport. The models describe observed quantitative structure-activity relationships (QSARs) well on the whole, confirming that partitioning is of key importance in in vivo behavior of a xenobiotic. The partition coefficient is shown to correlate with numerous other parameters representing bulk, such as molecular weight, volume and surface area, parachor and calculated indices such as molecular connectivity; this is especially so for apolar molecules, because for polar molecules lipophilicity factors into both bulk and polar or hydrogen bonding components. The relationship of partition coefficient to chromatographic parameters is discussed, and it is shown that such parameters, which are often readily obtainable experimentally, can successfully supplant partition coefficient in QSARs. The relationship of aqueous solubility with partition coefficient is examined in detail. Correlations are observed, even with solid compounds, and these can be used to predict solubility. The additive/constitutive nature of partition coefficient is discussed extensively, as are the available schemes for the calculation of partition coefficient. Finally the use of partition coefficient to provide structural information is considered. It is shown that partition coefficient can be a valuable structural tool, especially if the enthalpy and entropy of partitioning are available. PMID:3905374

  16. 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

  17. 3D-QSAR-Assisted Design, Synthesis, and Evaluation of Novobiocin Analogues

    PubMed Central

    2012-01-01

    Hsp90 is an attractive therapeutic target for the treatment of cancer. Extensive structural modifications to novobiocin, the first Hsp90 C-terminal inhibitor discovered, have produced a library of novobiocin analogues and revealed some structure–activity relationships. On the basis of the most potent novobiocin analogues generated from prior studies, a three-dimensional quantitative structure–activity (3D QSAR) model was built. In addition, a new set of novobiocin analogues containing various structural features supported by the 3D QSAR model were synthesized and evaluated against two breast cancer cell lines. Several new inhibitors produced antiproliferative activity at midnanomolar concentrations, which results through Hsp90 inhibition. PMID:23606927

  18. Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints.

    PubMed Central

    Jaworska, Joanna S; Comber, M; Auer, C; Van Leeuwen, C J

    2003-01-01

    The "Workshop on Regulatory Use of (Q)SARs for Human Health and Environmental Endpoints," organized by the European Chemical Industry Council and the International Council of Chemical Associations, gathered more than 60 human health and environmental experts from industry, academia, and regulatory agencies from around the world. They agreed, especially industry and regulatory authorities, that the workshop initiated great potential for the further development and use of predictive models, that is, quantitative structure-activity relationships [(Q)SARs], for chemicals management in a much broader scope than is currently the case. To increase confidence in (Q)SAR predictions and minimization of their misuse, the workshop aimed to develop proposals for guidance and acceptability criteria. The workshop also described the broad outline of a system that would apply that guidance and acceptability criteria to a (Q)SAR when used for chemical management purposes, including priority setting, risk assessment, and classification and labeling. PMID:12896859

  19. Quantitative structure-activity relationship of phenoxyphenyl-methanamine compounds with 5HT2A, SERT, and hERG activities.

    PubMed

    Mente, Scot; Gallaschun, Randall; Schmidt, Anne; Lebel, Lorrie; Vanase-Frawley, Michelle; Fliri, Anton

    2008-12-01

    QSAR models have been used to evaluate activities for compounds in the phenoxyphenyl-methanamine (PPMA) class of compounds. These models utilize Hammett-type donating-withdrawing substituent values as well as simple parameters to describe substituent size and elucidate the SAR of the 'A' and 'B' rings. Using this methodology, intuitive QSAR relationships were found for the three biological activities with R(2) values of 0.73, 0.45, and 0.58 for 5HT(2A), SerT, and hERG activities.

  20. Chemical domain of QSAR models from atom-centered fragments.

    PubMed

    Kühne, Ralph; Ebert, Ralf-Uwe; Schüürmann, Gerrit

    2009-12-01

    A methodology to characterize the chemical domain of qualitative and quantitative structure-activity relationship (QSAR) models based on the atom-centered fragment (ACF) approach is introduced. ACFs decompose the molecule into structural pieces, with each non-hydrogen atom of the molecule acting as an ACF center. ACFs vary with respect to their size in terms of the path length covered in each bonding direction starting from a given central atom and how comprehensively the neighbor atoms (including hydrogen) are described in terms of element type and bonding environment. In addition to these different levels of ACF definitions, the ACF match mode as degree of strictness of the ACF comparison between a test compound and a given ACF pool (such as from a training set) has to be specified. Analyses of the prediction statistics of three QSAR models with their training sets as well as with external test sets and associated subsets demonstrate a clear relationship between the prediction performance and the levels of ACF definition and match mode. The findings suggest that second-order ACFs combined with a borderline match mode may serve as a generic and at the same time a mechanistically sound tool to define and evaluate the chemical domain of QSAR models. Moreover, four standard categories of the ACF-based membership to a given chemical domain (outside, borderline outside, borderline inside, inside) are introduced that provide more specific information about the expected QSAR prediction performance. As such, the ACF-based characterization of the chemical domain appears to be particularly useful for QSAR applications in the context of REACH and other regulatory schemes addressing the safety evaluation of chemical compounds.

  1. Structure-molluscicidal activity relationships of acylphloroglucinols from ferns.

    PubMed

    Socolsky, Cecilia; Borkosky, Susana; Bardón, Alicia

    2011-03-01

    The molluscicidal activity of 12 phloroglucinol derivatives previously isolated from Elaphoglossum piloselloides, E. gayanum, E. yungense, and E. lindbergii, as well as 3 known acylphloroglucinols, now reported from an Argentine collection of Dryopteris wallichiana, was evaluated against the schistosomiasis vector snail Biomphalaria peregrina. Molluscicidal effects were analyzed and compared with those previously observed for 4 acylphloroglucinols from E. piloselloides and their corresponding peracetylated derivatives, in order to draw structure-activity relationships. The most active compounds were the prenylated desaspidins elaphogayanin B and elaphopilosins A and B (LD50 = 1.90, 2.90, and 0.94 ppm, respectively), together with the only evaluated prenylated para-aspidin, elaphopilosin C (LD50 = 2.15 ppm). Quantitative structure-activity relationships (QSAR) were studied by means of a semiempirical method (PM3) for the 24 natural phloroglucinol derivatives included in this paper. The descriptor molecular volume was found to have good correlation with the observed molluscicidal activity (r2 = 0.77). The derived equation can be considered useful to predict the molluscicidal activity of bi and tricyclic acylphloroglucinols. The QSAR analysis showed that there is an optimum volume for high activity, probably related to the size of a receptor's active site. Bigger molecules display lower activity.

  2. An alignment independent 3D QSAR study of the antiproliferative activity of 1,2,4,5-tetraoxanes.

    PubMed

    Cvijetić, Ilija N; Zizak, Zeljko P; Stanojković, Tatjana P; Juranić, Zorica D; Terzić, Natasa; Opsenica, Igor M; Opsenica, Dejan M; Juranić, Ivan O; Drakulić, Branko J

    2010-10-01

    An alignment-free 3D QSAR study on antiproliferative activity of the thirty-three 1,2,4,5-tetraoxane derivatives toward two human dedifferentiated cell lines was reported. GRIND methodology, where descriptors are derived from GRID molecular interaction fields (MIF), were used. It was found that pharmacophoric pattern attributed to the most potent derivatives include amido NH of the primary or secondary amide, and the acetoxy fragments at positions 7 and 12 of steroid core which are, along with the tetraoxane ring, common for all studied compounds. Independently, simple multiple regression model obtained by using the whole-molecular properties, confirmed that the hydrophobicity and the H-bond donor properties are the main parameters influencing potency of compounds toward human cervix carcinoma (HeLa) and human malignant melanoma (FemX) cell lines. Corollary, similar structural motifs are found to be important for the potency toward both examined cell lines.

  3. Acute toxicity and QSAR of chlorophenols on Daphnia magna

    SciTech Connect

    Devillers, J.; Chambon, P.

    1986-10-01

    Chlorophenols which are released into natural waters from various industrial processes and from agricultural uses have been recognized as a group of chemical substances potentially hazardous to the aquatic environment. Therefore it is important to estimate their toxic impact on biota. Thus, the scope of this research was to obtain acute toxicity data for seventeen chlorophenols towards Daphnia magna and to explore the possibilities of deriving QSAR's (quantitative structure-activity relationship) from the above values.

  4. 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.

  5. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

    NASA Astrophysics Data System (ADS)

    Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-03-01

    In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal

  6. The antibacterial activity of some sulfonamides and sulfonyl hydrazones, and 2D-QSAR study of a series of sulfonyl hydrazones

    NASA Astrophysics Data System (ADS)

    Aslan, H. Güzin; Özcan, Servet; Karacan, Nurcan

    2012-12-01

    Benzenesulfonicacid-1-methylhydrazide (1) and its four aromatic sulfonyl hydrazone derivatives (1a-1d), N-(3-amino-2-hydroxypropyl)benzene sulfonamide (2) and N-(2-hydroxyethyl)benzenesulfonamide (3) were synthesized and their structures were determined by IR, 1H NMR, 13C NMR, and LCMS techniques. Antibacterial activities of new synthesized compounds were evaluated against various bacteria strains by microdilution and disk diffusion methods. The experimental results show that presence of OH group on sulfonamides reduces the antimicrobial activity, and antimicrobial activities of the sulfonyl hydrazones (1a-1d) are smaller than that of the parent sulfonamide (1), except Candida albicans. In addition, 2D-QSAR analysis was performed on 28 aromatic sulfonyl hydrazones as antimicrobial agents against Escherichia coli and Staphylococcus aureus. In the QSAR models, the most important descriptor is total point-charge component of the molecular dipole for E. coli, and partial negative surface area (PNSA-1) for S. aureus.

  7. 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...

  8. Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella typhimurium TA100

    SciTech Connect

    Debnath, A.K.; Hansch, C. ); Shusterman, A.J. ); Lopez de Compadre, R.L. )

    1992-01-01

    A quantitative structure-activity relationship (QSAR) has been derived for the mutagenic activity of 117 aromatic and heteroaromatic nitro compounds acting on Salmonella typhimurium TA100. Relative mutagenic activity is bilinearly dependent on hydrophobicity, with an optimal log P of 5.44, and is linearly dependent on the energy of the lowest unoccupied molecular orbital of the nitro compound. The dependence of mutagenic activity on hydrophobicity and electronic effects is very similar for TA98 and TA100. Mutagenic activity in TA100 does not depend on the size of the aromatic ring system, as it does in TA98. The effect of the choice of assay organism, TA98 versus TA100, on nitroarene QSAR is seen to be similar to the effect previously found for aminoarenes. Lateral verification of QSARs is presented as a tool for establishing the significance of a new QSAR.

  9. Quantitative structure-activity relationships of antimicrobial fatty acids and derivatives against Staphylococcus aureus *

    PubMed Central

    Zhang, Hui; Zhang, Lu; Peng, Li-juan; Dong, Xiao-wu; Wu, Di; Wu, Vivian Chi-Hua; Feng, Feng-qin

    2012-01-01

    Fatty acids and derivatives (FADs) are resources for natural antimicrobials. In order to screen for additional potent antimicrobial agents, the antimicrobial activities of FADs against Staphylococcus aureus were examined using a microplate assay. Monoglycerides of fatty acids were the most potent class of fatty acids, among which monotridecanoin possessed the most potent antimicrobial activity. The conventional quantitative structure-activity relationship (QSAR) and comparative molecular field analysis (CoMFA) were performed to establish two statistically reliable models (conventional QSAR: R 2=0.942, Q 2 LOO=0.910; CoMFA: R 2=0.979, Q 2=0.588, respectively). Improved forecasting can be achieved by the combination of these two models that provide a good insight into the structure-activity relationships of the FADs and that may be useful to design new FADs as antimicrobial agents. PMID:22302421

  10. Quantitative structure-activity relationship studies of a series of sulfa drugs as inhibitors of Pneumocystis carinii dihydropteroate synthetase.

    PubMed

    Johnson, T; Khan, I A; Avery, M A; Grant, J; Meshnick, S R

    1998-06-01

    Sulfone and sulfanilamide sulfa drugs have been shown to inhibit dihydropteroate synthetase (DHPS) isolated from Pneumocystis carinii. In order to develop a pharmacophoric model for this inhibition, quantitative structure-activity relationships (QSAR) for sulfa drugs active against DHPS have been studied. Accurate 50% inhibitory concentrations were collected for 44 analogs, and other parameters, such as partition coefficients and molar refractivity, were calculated. Conventional multiple regression analysis of these data did not provide acceptable QSAR. However, three-dimensional QSAR provided by comparative molecular field analysis did give excellent results. Upon removal of poorly correlated analogs, a data set of 36 analogs, all having a common NHSO2 group, provided a cross-validated r2 value of 0.699 and conventional r2 value of 0.964. The resulting pharmacophore model should be useful for understanding and predicting the binding of DHPS by new sulfa drugs.

  11. [A new SVRDF 3D-descriptor of amino acids and its application to peptide quantitative structure activity relationship].

    PubMed

    Tong, Jian-Bo; Zhang, Sheng-Wan; Cheng, Su-Li; Li, Gai-Xian

    2007-01-01

    To establish a new amino acid structure descriptor that can be applied to polypeptide quantitative structure activity relationship (QSAR) studies, a new descriptor, SVRDF, was derived from a principal components analysis of a matrix of 150 radial distribution function index of amino acids. The scale was then applied in three panels of peptide QSAR that were molded by partial least squares regression. The obtained models with the correlation coefficients (R2(cum)), cross-validation correlation coefficients (Q2(cum)) were 0.766 and 0.724 for 48 bitter tasting dipeptides; 0.941 and 0.811 for 21 oxytocin analogues; 0.996 and 0.919 for 20 thromboplastin inhibitors. Satisfactory results showed that information related to biological activity can be systemically expressed by SVRDF scales, which may be an useful structural expression methodology for the study of peptides QSAR.

  12. Design, synthesis, crystal structure, insecticidal activity, molecular docking, and QSAR studies of novel N3-substituted imidacloprid derivatives.

    PubMed

    Wang, Mei-Juan; Zhao, Xiao-Bo; Wu, Dan; Liu, Ying-Qian; Zhang, Yan; Nan, Xiang; Liu, Huanxiang; Yu, Hai-Tao; Hu, Guan-Fang; Yan, Li-Ting

    2014-06-18

    Three novel series of N3-substituted imidacloprid derivatives were designed and synthesized, and their structures were identified on the basis of satisfactory analytical and spectral ((1)H NMR, (13)C NMR, MS, elemental analysis, and X-ray) data. Preliminary bioassays indicated that all of the derivatives exhibited significant insecticidal activities against Aphis craccivora, with LC50 values ranging from 0.00895 to 0.49947 mmol/L, and the insecticidal activities of some of them were comparable to those of the control imidacloprid. Some key structural features related to their insecticidal activities were identified, and the binding modes between target compounds and nAChR model were also further explored by molecular docking. By comparing the interaction features of imidacloprid and compound 26 with highest insecticidal activity, the origin of the high insecticidal activity of compound 26 was identified. On the basis of the conformations generated by molecular docking, a satisfactory 2D-QSAR model with six selected descriptors was built using genetic algorithm-multiple linear regression (GA-MLR) method. The analysis of the built model showed the molecular size, shape, and the ability to form hydrogen bond were important for insecticidal potency. The information obtained in the study will be very helpful for the design of new derivatives with high insecticidal activities.

  13. Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology.

    PubMed

    Singh, Kunwar P; Gupta, Shikha; Kumar, Anuj; Mohan, Dinesh

    2014-05-19

    The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.

  14. 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

  15. Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands

    NASA Astrophysics Data System (ADS)

    Datar, Prasanna A.; Khedkar, Santosh A.; Malde, Alpeshkumar K.; Coutinho, Evans C.

    2006-06-01

    A novel approach termed comparative residue-interaction analysis (CoRIA), emphasizing the trends and principles of QSAR in a ligand-receptor environment has been developed to analyze and predict the binding affinity of enzyme inhibitors. To test this new approach, a training set of 36 COX-2 inhibitors belonging to nine families was selected. The putative binding (bioactive) conformations of inhibitors in the COX-2 active site were searched using the program DOCK. The docked configurations were further refined by a combination of Monte Carlo and simulated annealing methods with the Affinity program. The non-bonded interaction energies of the inhibitors with the individual amino acid residues in the active site were then computed. These interaction energies, plus specific terms describing the thermodynamics of ligand-enzyme binding, were correlated to the biological activity with G/PLS. The various QSAR models obtained were validated internally by cross validation and boot strapping, and externally using a test set of 13 molecules. The QSAR models developed on the CoRIA formalism were robust with good r 2, q 2 and r pred 2 values. The major highlights of the method are: adaptation of the QSAR formalism in a receptor setting to answer both the type (qualitative) and the extent (quantitative) of ligand-receptor binding, and use of descriptors that account for the complete thermodynamics of the ligand-receptor binding. The CoRIA approach can be used to identify crucial interactions of inhibitors with the enzyme at the residue level, which can be gainfully exploited in optimizing the inhibitory activity of ligands. Furthermore, it can be used with advantage to guide point mutation studies. As regards the COX-2 dataset, the CoRIA approach shows that improving Coulombic interaction with Pro528 and reducing van der Waals interaction with Tyr385 will improve the binding affinity of inhibitors.

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

    SciTech Connect

    Li, Xiaolin; Ye, Li; Wang, Xiaoxiang; Wang, Xinzhou; Liu, Hongling; Zhu, Yongliang; Yu, Hongxia

    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 and 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.

  17. 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.

  18. QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms.

    PubMed

    Wang, Haikuan; Wang, Xiaojie; Li, Xiaolu; Zhang, Yehong; Dai, Yujie; Guo, Changlu; Zheng, Heng

    2012-09-28

    The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R(2)(adj)) respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R(2)(cv)) respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters.

  19. Quantitative structure-activity relationship studies on nitrofuranyl antitubercular agents

    PubMed Central

    Hevener, Kirk E.; Ball, David M.; Buolamwini, John K.

    2008-01-01

    A series of nitrofuranylamide and related aromatic compounds displaying potent activity against M. tuberculosis has been investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation, and high internal validity (cross validated r2 > .5) have been developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents. PMID:18701298

  20. Exhaustive Structure Generation for Inverse-QSPR/QSAR.

    PubMed

    Miyao, Tomoyuki; Arakawa, Masamoto; Funatsu, Kimito

    2010-01-12

    Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200 °C were exhaustively generated.

  1. 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

  2. The aquatic toxicity of anionic surfactants to Daphnia magna--a comparative QSAR study of linear alkylbenzene sulphonates and ester sulphonates.

    PubMed

    Hodges, Geoff; Roberts, David W; Marshall, Stuart J; Dearden, John C

    2006-06-01

    This paper develops quantitative structure activity relationships (QSARs) for the acute aquatic toxicity of the anionic surfactants linear alkylbenzene sulphonates (LAS) and ester sulphonates (ES) to Daphnia magna, the aim being to investigate the modes of action by comparing the QSARs for the two types of surfactant. The generated data for ES have been used to develop a QSAR correlating toxicity with calculated log P values: log(1/EC50)= 0.78 log P+1.37. This equation has an intercept 1.1 log units lower than a QSAR for linear alkylbenzene sulphonates (LAS). The findings suggest that either ES surfactants act by a different mode of action to LAS and other anionic surfactants or the log P calculation method introduces a systematic overestimate when applied to ES.

  3. Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides

    PubMed Central

    Nie, Kaiying; Wang, Zhaojing

    2016-01-01

    In this study, quantitative structure activity relationship (QSAR) models for the antioxidant activity of polysaccharides were developed with 50% effective concentration (EC50) as the dependent variable. To establish optimum QSAR models, multiple linear regressions (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used, and 11 molecular descriptors were selected. The optimum QSAR model for predicting EC50 of DPPH-scavenging activity consisted of four major descriptors. MLR model gave EC50 = 0.033Ara-0.041GalA-0.03GlcA-0.025PC+0.484, and MLR fitted the training set with R = 0.807. ANN model gave the improvement of training set (R = 0.96, RMSE = 0.018) and test set (R = 0.933, RMSE = 0.055) which indicated that it was more accurately than SVM and MLR models for predicting the DPPH-scavenging activity of polysaccharides. 67 compounds were used for predicting EC50 of the hydroxyl radicals scavenging activity of polysaccharides. MLR model gave EC50 = 0.12PC+0.083Fuc+0.013Rha-0.02UA+0.372. A comparison of results from models indicated that ANN model (R = 0.944, RMSE = 0.119) was also the best one for predicting the hydroxyl radicals scavenging activity of polysaccharides. MLR and ANN models showed that Ara and GalA appeared critical in determining EC50 of DPPH-scavenging activity, and Fuc, Rha, uronic acid and protein content had a great effect on the hydroxyl radicals scavenging activity of polysaccharides. The antioxidant activity of polysaccharide usually was high in MW range of 4000–100000, and the antioxidant activity could be affected simultaneously by other polysaccharide properties, such as uronic acid and Ara. PMID:27685320

  4. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models.

    PubMed

    Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz

    2016-04-07

    In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase "correlation does not imply causation" reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.

  5. Synthesis, antimicrobial activity and QSAR studies of new 2,3-disubstituted-3,3a,4,5,6,7-hexahydro-2H-indazoles.

    PubMed

    Minu, Maninder; Thangadurai, Ananda; Wakode, Sharad Ramesh; Agrawal, Shyam Sundar; Narasimhan, Balasubramanian

    2009-06-01

    Antimicrobial activity of synthesized 2,3-disubstituted-3,3a,4,5,6,7-hexahydro-2H-indazole derivatives indicated that 3-(4-chlorophenyl)-2-(4-nitrophenylsulfonyl)-3,3a,4,5,6,7-hexahydro-2H-indazole (6) and 3-(4-fluorophenyl)-2-(4-nitrophenylsulfonyl)-3,3a,4,5,6,7-hexahydro-2H-indazole (20) were the most active compounds. Further, the results of QSAR studies indicated the importance of topological parameters (2)chi and (2)chi(v) in defining the antimicrobial activity of hexahydroindazoles.

  6. 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 (LC50) 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 LC50 using chemicals that were independently classified to act according to the non-specific mode of action of baseline toxicity. The octanol-water partition coefficient Kow is commonly applied to discriminate between non-polar and polar narcotics. Replacing the Kow by the liposome-water partition coefficient Klipw 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 (ILC50) and chemical activity (La50) 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.

  7. Comparison between 5,10,15,20-tetraaryl- and 5,15-diarylporphyrins as photosensitizers: synthesis, photodynamic activity, and quantitative structure-activity relationship modeling.

    PubMed

    Banfi, Stefano; Caruso, Enrico; Buccafurni, Loredana; Murano, Roberto; Monti, Elena; Gariboldi, Marzia; Papa, Ester; Gramatica, Paola

    2006-06-01

    The synthesis of a panel of seven nonsymmetric 5,10,15,20-tetraarylporphyrins, 13 symmetric and nonsymmetric 5,15-diarylporphyrins, and one 5,15-diarylchlorin is described. In vitro photodynamic activities on HCT116 human colon adenocarcinoma cells were evaluated by standard cytotoxicity assays. A predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, of a series of 34 tetrapyrrolic photosensitizers (PSs), including the 24 compounds synthesized in this work, was developed to describe the relationship between structural features and photodynamic activity. The present study demonstrates that structural features significantly influence the photodynamic activity of tetrapyrrolic derivatives: diaryl compounds were more active with respect to the tetraarylporphyrins, and among the diaryl derivatives, hydroxy-substituted compounds were more effective than the corresponding methoxy-substituted ones. Furthermore, three monoarylporphyrins, isolated as byproducts during diarylporphyrin synthesis, were considered for both photodynamic and QSAR studies; surprisingly they were found to be particularly active photosensitizers.

  8. Comparison of quantitative structure-activity relationship model performances on carboquinone derivatives.

    PubMed

    Bolboacă, Sorana-Daniela; Jäntschi, Lorentz

    2009-10-14

    Quantitative structure-activity relationship (qSAR) models are used to understand how the structure and activity of chemical compounds relate. In the present study, 37 carboquinone derivatives were evaluated and two different qSAR models were developed using members of the Molecular Descriptors Family (MDF) and the Molecular Descriptors Family on Vertices (MDFV). The usual parameters of regression models and the following estimators were defined and calculated in order to analyze the validity and to compare the models: Akaike's information criteria (three parameters), Schwarz (or Bayesian) information criterion, Amemiya prediction criterion, Hannan-Quinn criterion, Kubinyi function, Steiger's Z test, and Akaike's weights. The MDF and MDFV models proved to have the same estimation ability of the goodness-of-fit according to Steiger's Z test. The MDFV model proved to be the best model for the considered carboquinone derivatives according to the defined information and prediction criteria, Kubinyi function, and Akaike's weights.

  9. Ranking of aquatic toxicity of esters modelled by QSAR.

    PubMed

    Papa, Ester; Battaini, Francesca; Gramatica, Paola

    2005-02-01

    Alternative methods like predictions based on Quantitative Structure-Activity Relationships (QSARs) are now accepted to fill data gaps and define priority lists for more expensive and time consuming assessments. A heterogeneous data set of 74 esters was studied for their aquatic toxicity, and available experimental toxicity data on algae, Daphnia and fish were used to develop statistically validated QSAR models, obtained using multiple linear regression (MLR) by the OLS (Ordinary Least Squares) method and GA-VSS (Variable Subset Selection by Genetic Algorithms) to predict missing values. An ESter Aquatic Toxicity INdex (ESATIN) was then obtained by combining, by PCA, experimental and predicted toxicity data, from which model outliers and esters highly influential due to their structure had been eliminated. Finally this integrated aquatic toxicity index, defined by the PC1 score, was modelled using only a few theoretical molecular descriptors. This last QSAR model, statistically validated for its predictive power, could be proposed as a preliminary evaluative method for screening/prioritising esters according to their integrated aquatic toxicity, just starting from their molecular structure.

  10. Systematic generation of chemical structures for rational drug design based on QSAR models.

    PubMed

    Funatsu, Kimito; Miyao, Tomoyuki; Arakawa, Masamoto

    2011-03-01

    The first step in the process of drug development is to determine those lead compounds that demonstrate significant biological activity with regard to a target protein. Because this process is often costly and time consuming, there is a need to develop efficient methodologies for the generation of lead compounds for practical drug design. One promising approach for determining a potent lead compound is computational virtual screening. The biological activities of candidate structures found in virtual libraries are estimated by using quantitative structure activity relationship (QSAR) models and/or computational docking simulations. In virtual screening studies, databases of existing drugs or natural products are commonly used as a source of lead candidates. However, these databases are not sufficient for the purpose of finding lead candidates having novel scaffolds. Therefore, a method must be developed to generate novel molecular structures to indicate high activity for efficient lead discovery. In this paper, we review current trends in structure generation methods for drug design and discuss future directions. First, we present an overview of lead discovery and drug design, and then, we review structure generation methods. Here, the structure generation methods are classified on the basis of whether or not they employ QSAR models for generating structures. We conclude that the use of QSAR models for structure generation is an effective method for computational lead discovery. Finally, we discuss the problems regarding the applicability domain of QSAR models and future directions in this field.

  11. 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

  12. Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

    PubMed

    Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

    2013-06-01

    Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

  13. A descriptor of amino acids: SVRG and its application to peptide quantitative structure-activity relationship.

    PubMed

    Tong, J; Che, T; Li, Y; Wang, P; Xu, X; Chen, Y

    2011-01-01

    In this work, a descriptor, SVRG (principal component scores vector of radial distribution function descriptors and geometrical descriptors), was derived from principal component analysis (PCA) of a matrix of two structural variables of coded amino acids, including radial distribution function index (RDF) and geometrical index. SVRG scales were then applied in three panels of peptide quantitative structure-activity relationships (QSARs) which were modelled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum)), cross-validation correlation coefficient (Q²(LOO)) were 0.910 and 0.863 for 48 bitter-tasting dipeptides; 0.968 and 0.931 for 21 oxytocin analogues; and 0.992 and 0.954 for 20 thromboplastin inhibitors. Satisfactory results showed that SVRG contained much chemical information relating to bioactivities. The approach may be a useful structural expression methodology for studies on peptide QSAR.

  14. 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.

  15. QSAR and molecular docking studies on oxindole derivatives as VEGFR-2 tyrosine kinase inhibitors.

    PubMed

    Kang, Cong-Min; Liu, Dong-Qing; Zhao, Xu-Hao; Dai, Ying-Jie; Cheng, Jia-Gao; Lv, Ying-Tao

    2016-01-01

    The three-dimensional quantitative structure-activity relationships (3D-QSAR) were established for 30 oxindole derivatives as vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis comparative molecular similarity indices analysis (CoMSIA) techniques. With the CoMFA model, the cross-validated value (q(2)) was 0.777, the non-cross-validated value (R(2)) was 0.987, and the external cross-validated value ([Formula: see text]) was 0.72. And with the CoMSIA model, the corresponding q(2), R(2) and [Formula: see text] values were 0.710, 0.988 and 0.78, respectively. Docking studies were employed to bind the inhibitors into the active site to determine the probable binding conformation. The binding mode obtained by molecular docking was in good agreement with the 3D-QSAR results. Based on the QSAR models and the docking binding mode, a set of new VEGFR-2 tyrosine kinase inhibitors were designed, which showed excellent predicting inhibiting potencies. The result revealed that both QSAR models have good predictive capability to guide the design and structural modification of homologic compounds. It is also helpful for further research and development of new VEGFR-2 tyrosine kinase inhibitors.

  16. Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout

    NASA Astrophysics Data System (ADS)

    Mendenhall, Jeffrey; Meiler, Jens

    2016-02-01

    Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

  17. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function

    SciTech Connect

    Schormann, N.; Senkovich, O.; Walker, K.; Wright, D.L.; Anderson, A.C.; Rosowsky, A.; Ananthan, S.; Shinkre, B.; Velu, S.; Chattopadhyay, D.

    2009-07-10

    We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.

  18. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function.

    PubMed

    Schormann, N; Senkovich, O; Walker, K; Wright, D L; Anderson, A C; Rosowsky, A; Ananthan, S; Shinkre, B; Velu, S; Chattopadhyay, D

    2008-12-01

    We have employed a structure-based three-dimensional quantitative structure-activity relationship (3D-QSAR) approach to predict the biochemical activity for inhibitors of T. cruzi dihydrofolate reductase-thymidylate synthase (DHFR-TS). Crystal structures of complexes of the enzyme with eight different inhibitors of the DHFR activity together with the structure in the substrate-free state (DHFR domain) were used to validate and refine docking poses of ligands that constitute likely active conformations. Structural information from these complexes formed the basis for the structure-based alignment used as input for the QSAR study. Contrary to indirect ligand-based approaches the strategy described here employs a direct receptor-based approach. The goal is to generate a library of selective lead inhibitors for further development as antiparasitic agents. 3D-QSAR models were obtained for T. cruzi DHFR-TS (30 inhibitors in learning set) and human DHFR (36 inhibitors in learning set) that show a very good agreement between experimental and predicted enzyme inhibition data. For crossvalidation of the QSAR model(s), we have used the 10% leave-one-out method. The derived 3D-QSAR models were tested against a few selected compounds (a small test set of six inhibitors for each enzyme) with known activity, which were not part of the learning set, and the quality of prediction of the initial 3D-QSAR models demonstrated that such studies are feasible. Further refinement of the models through integration of additional activity data and optimization of reliable docking poses is expected to lead to an improved predictive ability.

  19. 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

  20. 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

  1. Synthesis, QSAR and calcium channel antagonist activity of new 1,4-dihydropyridine derivatives containing 1-methyl-4,5-dichloroimidazolyl substituents.

    PubMed

    Hosseini, Maryam; Miri, Ramin; Amini, Mohsen; Mirkhani, Hossein; Hemmateenejad, Bahram; Ghodsi, Shahram; Alipour, Eskandar; Shafiee, Abbas

    2007-10-01

    A group of dialkyl and diarylester analogues of nifedipine, in which the ortho-nitrophenyl group at position 4 was replaced by a 1-methyl-4,5-dichloroimidazolyl substituent, were synthesized and evaluated as calcium-channel antagonists using the high K(+)concentration of guinea-pig ileum longitudinal smooth muscle. The structure of all compounds was confirmed by IR,(1)H-NMR, and mass spectra. The calcium-channel antagonist activity of compounds 10a-f demonstrated that compound 10b was the most active and 10f the least active one. With unsymmetrical diesters 12a-k, the most active compound was the ethyl, phenethyl derivative. Structural parameters on the calcium-channel antagonist activity were evaluated by QSAR analysis and a linear correlation was found between the -log IC(50) values of these compounds and their constitutional and topological properties.

  2. 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...

  3. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

    PubMed

    Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

    2016-02-13

    Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

  4. Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi.

    PubMed

    Guido, Rafael V C; Trossini, Gustavo H G; Castilho, Marcelo S; Oliva, Glaucius; Ferreira, Elizabeth I; Andricopulo, Adriano D

    2008-12-01

    Chagas' disease is a parasitic infection widely distributed throughout Latin America, with devastating consequences in terms of human morbidity and mortality. Cruzain, the major cysteine protease from Trypanosoma cruzi, is an attractive target for antitrypanosomal chemotherapy. In the present work, classical two-dimensional quantitative structure-activity relationships (2D QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 45 thiosemicarbazone and semicarbazone derivatives as inhibitors of T. cruzi cruzain. Significant statistical models (HQSAR, q(2) = 0.75 and r(2) = 0.96; classical QSAR, q(2) = 0.72 and r(2) = 0.83) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 10 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(2)(pred) = 0.95; classical QSAR, r(2)(pred) = 0.91), indicating the existence of complementary between the two ligand-based drug design techniques.

  5. Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study

    PubMed Central

    Satpathy, Raghunath; Guru, R. K.; Behera, R.; Nayak, B.

    2015-01-01

    Context: 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. Aims: To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. Materials and Methods: 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. Statistical Analysis Used: Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. Results: 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. Conclusions: Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. PMID:25709332

  6. 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.

  7. Improving confidence in (Q)SAR predictions under Canada's Chemicals Management Plan - a chemical space approach.

    PubMed

    Kulkarni, S A; Benfenati, E; Barton-Maclaren, T S

    2016-10-20

    One of the key challenges of Canada's Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model's ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model's predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.

  8. A computational quantitative structure-activity relationship study of carbamate anticonvulsants using quantum pharmacological methods.

    PubMed

    Knight, J L; Weaver, D F

    1998-10-01

    A pattern recognition quantitative structure-activity relationship (QSAR) study has been performed to determine the molecular features of carbamate anticonvulsants which influence biological activity. Although carbamates, such as felbamate, have been used to treat epilepsy, their mechanisms of efficacy and toxicity are not completely understood. Quantum and classical mechanics calculations have been exploited to describe 46 carbamate drugs. Employing a principal component analysis and multiple linear regression calculations, five crucial structural descriptors were identified which directly relate to the bioactivity of the carbamate family. With the resulting mathematical model, the biological activity of carbamate analogues can be predicted with 85-90% accuracy.

  9. Pharmacophore modelling, atom-based 3D-QSAR generation and virtual screening of molecules projected for mPGES-1 inhibitory activity.

    PubMed

    Misra, S; Saini, M; Ojha, H; Sharma, D; Sharma, K

    2017-01-01

    COX-2 inhibitors exhibit anticancer effects in various cancer models but due to the adverse side effects associated with these inhibitors, targeting molecules downstream of COX-2 (such as mPGES-1) has been suggested. Even after calls for mPGES-1 inhibitor design, to date there are only a few published inhibitors targeting the enzyme and displaying anticancer activity. In the present study, we have deployed both ligand and structure-based drug design approaches to hunt novel drug-like candidates as mPGES-1 inhibitors. Fifty-four compounds with tested mPGES-1 inhibitory value were used to develop a model with four pharmacophoric features. 3D-QSAR studies were undertaken to check the robustness of the model. Statistical parameters such as r(2) = 0.9924, q(2) = 0.5761 and F test = 1139.7 indicated significant predictive ability of the proposed model. Our QSAR model exhibits sites where a hydrogen bond donor, hydrophobic group and the aromatic ring can be substituted so as to enhance the efficacy of the inhibitor. Furthermore, we used our validated pharmacophore model as a three-dimensional query to screen the FDA-approved Lopac database. Finally, five compounds were selected as potent mPGES-1 inhibitors on the basis of their docking energy and pharmacokinetic properties such as ADME and Lipinski rule of five.

  10. Microwave assistant one pot synthesis, crystal structure, antifungal activities and 3D-QSAR of novel 1,2,4-triazolo[4,3-a]pyridines.

    PubMed

    Liu, Xing-Hai; Sun, Zhao-Hui; Yang, Ming-Yan; Tan, Cheng-Xia; Weng, Jian-Quan; Zhang, Yong-Gang; Ma, Yi

    2014-09-01

    A series of novel 1,2,4-triazolo[4,3-a]pyridines were synthesized, and their structures were characterized by (1) H NMR, MS, elemental analysis, and single-crystal X-ray diffraction analysis. The antifungal activities were evaluated. The antifungal activity results indicated that the compound 2b, 2g, 2p, and 2i exhibited good activities. The activity of compound 2b, 2g, 2p, and 2i can compare with the commercial pesticide. The 3D-QSAR model was developed using CoMFA method. Both the steric and electronic field distributions of CoMFA are in good agreement in this work and will be very helpful in designing a new set of analogues.

  11. Assessment of the Potential Biological Activity of Low Molecular Weight Metabolites of Freshwater Macrophytes with QSAR

    PubMed Central

    Fedorova, Elena V.; Krylova, Julia V.

    2016-01-01

    The paper focuses on the assessment of the spectrum of biological activities (antineoplastic, anti-inflammatory, antifungal, and antibacterial) with PASS (Prediction of Activity Spectra for Substances) for the major components of three macrophytes widespread in the Holarctic species of freshwater, emergent macrophyte with floating leaves, Nuphar lutea (L.) Sm., and two species of submergent macrophyte groups, Ceratophyllum demersum L. and Potamogeton obtusifolius (Mert. et Koch), for the discovery of their ecological and pharmacological potential. The predicted probability of anti-inflammatory or antineoplastic activities above 0.8 was observed for twenty compounds. The same compounds were also characterized by high probability of antifungal and antibacterial activity. Six metabolites, namely, hexanal, pentadecanal, tetradecanoic acid, dibutyl phthalate, hexadecanoic acid, and manool, were a part of the major components of all three studied plants, indicating their high ecological significance and a certain universalism in their use by various species of water plants for the implementation of ecological and biochemical functions. This report underlines the role of identified compounds not only as important components in regulation of biochemical and metabolic pathways and processes in aquatic ecological systems, but also as potential pharmacological agents in the fight against different diseases. PMID:27200207

  12. Quantitative structure-activity relationship modelling of oral acute toxicity and cytotoxic activity of fragrance materials in rodents.

    PubMed

    Papa, E; Luini, M; Gramatica, P

    2009-10-01

    Fragrance materials are used as ingredients in many consumer and personal care products. The wide and daily use of these substances, as well as their mainly uncontrolled discharge through domestic sewage, make fragrance materials both potential indoor and outdoor air pollutants which are also connected to possible toxic effects on humans (asthma, allergies, headaches). Unfortunately, little is known about the environmental fate and toxicity of these substances. However, the use of alternative, predictive approaches, such as quantitative structure-activity relationships (QSARs), can help in filling the data gap and in the characterization of the environmental and toxicological profile of these substances. In the proposed study, ordinary least squares regression-based QSAR models were developed for three toxicological endpoints: mouse oral LD(50), inhibition of NADH-oxidase (EC(50) NADH-Ox) and the effect on mitochondrial membrane potential (EC(50) DeltaPsim). Theoretical molecular descriptors were calculated by using DRAGON software, and the best QSAR models were developed according to the principles defined by the Organization for Economic Co-operation and Development.

  13. Analysis of stereoelectronic properties, mechanism of action and pharmacophore of synthetic indolo[2,1-b]quinazoline-6,12-dione derivatives in relation to antileishmanial activity using quantum chemical, cyclic voltammetry and 3-D-QSAR CATALYST procedures.

    PubMed

    Bhattacharjee, Apurba K; Skanchy, David J; Jennings, Barton; Hudson, Thomas H; Brendle, James J; Werbovetz, Karl A

    2002-06-01

    Several indolo[2,1-b]quinazoline-6,12-dione (tryptanthrin) derivatives exhibited remarkable activity at concentrations below 100 ng/mL when tested against in vitro Leishmania donovani amastigotes. The in vitro toxicity studies indicate that the compounds are fairly well tolerated in both macrophage and neuronal lines. An analysis based on qualitative and quantitative structure-activity relationship studies between in vitro antileishmanial activity and molecular electronic structure of 27 analogues of indolo[2,1-b]quinazoline-6,12-dione is presented here by using a combination of semi-empirical AM1 quantum chemical, cyclic voltammetry and a pharmacophore generation (CATALYST) methods. A modest to good correlation is observed between activity and a few calculated molecular properties such as molecular density, octanol-water partition coefficient, molecular orbital energies, and redox potentials. Electron transfer seems to be a plausible path in the mechanism of action of the compounds. A pharmacophore generated by using the 3-D QSAR of CATALYST produced a fairly accurate predictive model of antileishmanial activity of the tryptanthrins. The validity of the pharmacophore model extends to structurally different class of compounds that could open new frontiers for study. The carbonyl group of the five- and six-membered rings in the indolo[2,1-b]quinazoline-6,12-dione skeleton and the electron transfer ability to the carbonyl atom appear to be crucial for activity.

  14. Quantitative structure-activity relationship of organophosphate compounds based on molecular interaction fields descriptors.

    PubMed

    Zhao, Jinsong; Yu, Shuxia

    2013-03-01

    By using multi-block partial least-squares (MBPLS) method, quantitative structure-activity relationship (QSAR) between 35 organophosphate compounds (OP) and their 24h acute toxicities towards the housefly (Musca nebulo L.) was built on the molecular interaction fields (MIF) descriptors, which were obtained with O, N and DRY as probes, and then normalised with block unscaled weights (BUW) technique. The best QSAR model had 8 principal components, with the coefficient of determination R(2)=0.995 and that of leave-one-out cross-validation Q(2)=0.865, and the corresponding standard deviation of error 0.076 and 0.361, respectively. Block importance in the prediction (BIP) for O, N and DRY probe were 1.030, 0.962 and 1.007, respectively. Contour map of variable coefficients showed that hydrogen bonding between the O atom in PO and the NH groups in acetylcholinesterase (AChE) played an important role in the interaction between OP and AChE. Meanwhile, the hydrophobicity of OP also had significant contribution. QSAR based on the MIF descriptors could be a potential means to interpret the mechanisms of ligand-receptor interaction when the receptor was well known.

  15. Quantitative Structure-Activity Relationships for the Nucleophilicity of Trivalent Boron Compounds.

    PubMed

    García-López, Diego; Cid, Jessica; Marqués, Ruben; Fernández, Elena; Carbó, Jorge J

    2017-04-11

    We describe herein the development of quantitative structure-activity relationships (QSAR) for the nucleophilicity of trivalent boron compounds covering boryl fragments bonded to alkali and alkaline-earth metals, to transition metals, and to sp(3) boron units in diboron reagents. We used the charge of the boryl fragment (q[B]) and the boron p/s population ratio (p/s) to describe the electronic structures of boryl moieties, whereas the distance-weighted volume (Vw ) descriptor was used to evaluate the steric effects. The three-term easy-to-interpret QSAR model showed statistical significance and predictive ability (r(2) =0.88, q(2) =0.83). The use of chemically meaningful descriptors has allowed identification of the factors governing the boron nucleophilicity and indicates that the most efficient nucleophiles are those with enhanced the polarization of the B-X bond towards the boron atom and reduced steric bulk. A detailed analysis of the potential energy surfaces of different types of boron substituents has provided insight into the mechanism and established an order of nucleophilicity for boron in B-X: X=Li>Cu>B(sp(3) )>Pd. Finally, we used the QSAR model to make a priori predictions of experimentally untested compounds.

  16. The prospects for using (Q)SARs in a changing political environment--high expectations and a key role for the European Commission's joint research centre.

    PubMed

    Worth, A P; Van Leeuwen, C J; Hartung, T

    2004-01-01

    Recent policy developments in the European union (EU) and within the Organisation for Economic Cooperation and Development (OECD) have placed increased emphasis on the use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, within various regulatory programmes for the assessment of chemicals and products. The most significant example within the EU is the European commission's proposal (of 29 October 2003) to introduce a new system for managing chemicals (called REACH), which calls for an increased use of (Q)SARs and other non-animal methods, especially for the assessment of low production volume chemicals. Another development within the EU is the Seventh Amendment to the Cosmetics Directive, which foresees the phasing out of animal testing on cosmetics, combined with the imposition of marketing bans on cosmetics that have been tested on animals after certain deadlines. At the same time, the Existing Chemicals programme within the OECD is investigating ways of increasing the use of chemical category approaches, which depend heavily on the use of (Q)SARs, activity-activity relationships and read-across. Such developments are placing an enormous challenge on industry, regulatory bodies, and on the European commission's Joint Research Centre (JRC), which is responsible for providing independent scientific advice to policy makers in the European Commission and the Member States. This paper reviews the different scientific and regulatory purposes for which reliable (Q)SARs could be used, and describes the current work of the JRC in providing scientific support for the development, validation and implementation of (Q)SARs.

  17. 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.

  18. [Perspective of predictive toxicity assessment of in vivo repeated dose toxicity using structural activity relationship].

    PubMed

    Ono, Atsushi

    2010-01-01

    Tens of thousands of existing chemicals have been widely used for manufacture, agriculture, household and other purposes in worldwide. Only approximately 10% of chemicals have been assessed for human health hazard. The health hazard assessment of residual large number of chemicals for which little or no information of their toxicity is available is urgently needed for public health. However, the conduct of traditional toxicity tests which involves using animals for all of these chemicals would be economically impractical and ethically unacceptable. (Quantitative) Structure-Activity Relationships [(Q)SARs] are expected as method to have the potential to estimate hazards of chemicals from their structure, while reducing time, cost and animal testing currently needed. Therefore, our studies have been focused on evaluation of available (Q)SAR systems for estimating in vivo repeated toxicity on the liver. The results from our preliminary analysis showed the distribution for LogP of the chemicals which have potential to induce liver toxicity was bell-shape and indicating the possibility to estimate liver toxicity of chemicals from their physicochemical property. We have developed (Q)SAR models to in vivo liver toxicity using three commercially available systems (DEREK, ADMEWorks and MultiCASE) as well as combinatorial use of publically available chemoinformatic tools (CDK, MOSS and WEKA). Distinct data-sets of the 28-day repeated dose toxicity test of new and existing chemicals evaluated in Japan were used for model development and performance test. The results that concordances of commercial systems and public tools were almost same which below 70% may suggest currently attainable knowledge of in silico estimation of complex biological process, though it possible to obtain complementary and enhanced performance by combining predictions from different programs. In future, the combinatorial application of in silico and in vitro tests might provide more accurate

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  20. Review of synthesis, biological assay and QSAR studies of β-secretase inhibitors.

    PubMed

    Niño, Helena; García-Pintos, Isela; Rodríguez-Borges, José E; Escobar-Cubiella, Manolo; García-Mera, Xerardo; Prado-Prado, Francisco

    2011-12-01

    Alzheimer's disease (AD) is highly complex. While several pathologies characterize this disease, amyloid plaques, composed of the β-amyloid peptide, are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. The BACE-1 enzyme is essential for the generation of β-amyloid. BACE-1 knockout mice do not produce β-amyloid and are free from Alzheimer's associated pathologies, including neuronal loss and certain memory deficits. The fact that BACE-1 initiates the formation of β-amyloid, and the observation that BACE-1 levels are elevated in this disease provide direct and compelling reasons to develop therapies directed at BACE-1 inhibition, thus reducing β-amyloid and its associated toxicities. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these β-secretase inhibitors. QSAR models are necessary in order to guide the β-secretase synthesis. This work is aimed at reviewing different design and synthesis and computational studies for a very large and heterogeneous series of β-secretase inhibitors. First, we review design, synthesis, and Biological assay of β-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find out the structural requirements. Next, we review QSAR studies using the method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for receptor binding for β- secretase inhibitors.

  1. QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish.

    PubMed

    Zvinavashe, Elton; van den Berg, Hans; Soffers, Ans E M F; Vervoort, Jacques; Freidig, Andreas; Murk, Albertinka J; Rietjens, Ivonne M C M

    2008-03-01

    Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of Chemicals (REACH). The objective of the present study was to generate in vitro acute toxicity data that could be used to develop a QSAR model to describe acute in vivo toxicity of chlorinated alkanes. Cytotoxicity of a series of chlorinated alkanes to Chinese hamster ovary (CHO) cells was observed at concentrations similar to those that have been shown previously to be toxic to fish. Strong correlations exist between the acute in vitro toxicity of the chlorinated alkanes and (i) hydrophobicity [modeled by the calculated log K ow (octanol-water partition coefficient); r (2) = 0.883 and r int (2) = 0.854] and (ii) in vivo acute toxicity to fish ( r (2) = 0.758). A QSAR model has been developed to predict in vivo acute toxicity to fish, based on the in vitro data and even on in silico log K ow data only. The developed QSAR model is applicable to chlorinated alkanes with up to 10 carbon atoms, up to eight chlorine atoms, and log K ow values lying within the range from 1.71 to 5.70. Out of the 100204 compounds on the European Inventory of Existing Chemicals (EINECS), our QSAR model covers 77 (0.1%) of them. Our findings demonstrate that in vitro experiments and even in silico calculations can replace animal experiments in the prediction of the acute toxicity of chlorinated alkanes.

  2. 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.

  3. A QSAR model for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors) by nanofiltration membranes.

    PubMed

    Yangali-Quintanilla, Victor; Sadmani, Anwar; McConville, Megan; Kennedy, Maria; Amy, Gary

    2010-01-01

    A quantitative structure activity relationship (QSAR) model has been produced for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors, pesticides and other organic compounds) by polyamide nanofiltration (NF) membranes. Principal component analysis, partial least square regression and multiple linear regressions were used to find a general QSAR equation that combines interactions between membrane characteristics, filtration operating conditions and compound properties for predicting rejection. Membrane characteristics related to hydrophobicity (contact angle), salt rejection, and surface charge (zeta potential); compound properties describing hydrophobicity (log K(ow), log D), polarity (dipole moment), and size (molar volume, molecular length, molecular depth, equivalent width, molecular weight); and operating conditions namely flux, pressure, cross flow velocity, back diffusion mass transfer coefficient, hydrodynamic ratio (J(o)/k), and recovery were identified as candidate variables for rejection prediction. An experimental database produced by the authors that accounts for 106 rejection cases of emerging contaminants by NF membranes as result of eight experiments with clean and fouled membranes (NF-90, NF-200) was used to produce the QSAR model. Subsequently, using the QSAR model, rejection predictions were made for external experimental databases. Actual rejections were compared against predicted rejections and acceptable R(2) correlation coefficients were found (0.75 and 0.84) for the best models. Additionally, leave-one-out cross-validation of the models achieved a Q(2) of 0.72 for internal validation. In conclusion, a unified general QSAR equation was able to predict rejections of emerging contaminants during nanofiltration; moreover the present approach is a basis to continue investigation using multivariate analysis techniques for understanding membrane rejection of organic compounds.

  4. Nanomaterials - the Next Great Challenge for Qsar Modelers

    NASA Astrophysics Data System (ADS)

    Puzyn, Tomasz; Gajewicz, Agnieszka; Leszczynska, Danuta; Leszczynski, Jerzy

    In this final chapter a new perspective for the application of QSAR in the nanosciences is discussed. The role of nanomaterials is rapidly increasing in many aspects of everyday life. This is promoting a wide range of research needs related to both the design of new materials with required properties and performing a comprehensive risk assessment of the manufactured nanoparticles. The development of nanoscience also opens new areas for QSAR modelers. We have begun this contribution with a detailed discussion on the remarkable physical-chemical properties of nanomaterials and their specific toxicities. Both these factors should be considered as potential endpoints for further nano-QSAR studies. Then, we have highlighted the status and research needs in the area of molecular descriptors applicable to nanomaterials. Finally, we have put together currently available nano-QSAR models related to the physico-chemical endpoints of nanoparticles and their activity. Although we have observed many problems (i.e., a lack of experimental data, insufficient and inadequate descriptors), we do believe that application of QSAR methodology will significantly support nanoscience in the near future. Development of reliable nano-QSARs can be considered as the next challenging task for the QSAR community.

  5. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity.

    PubMed

    Gramatica, P; Pilutti, P; Papa, E

    2007-01-01

    Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.

  6. Fragment-based strategy for structural optimization in combination with 3D-QSAR.

    PubMed

    Yuan, Haoliang; Tai, Wenting; Hu, Shihe; Liu, Haichun; Zhang, Yanmin; Yao, Sihui; Ran, Ting; Lu, Shuai; Ke, Zhipeng; Xiong, Xiao; Xu, Jinxing; Chen, Yadong; Lu, Tao

    2013-10-01

    Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure-activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.

  7. Fragment-based strategy for structural optimization in combination with 3D-QSAR

    NASA Astrophysics Data System (ADS)

    Yuan, Haoliang; Tai, Wenting; Hu, Shihe; Liu, Haichun; Zhang, Yanmin; Yao, Sihui; Ran, Ting; Lu, Shuai; Ke, Zhipeng; Xiong, Xiao; Xu, Jinxing; Chen, Yadong; Lu, Tao

    2013-10-01

    Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure-activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.

  8. Local indices for similarity analysis (LISA)-a 3D-QSAR formalism based on local molecular similarity.

    PubMed

    Verma, Jitender; Malde, Alpeshkumar; Khedkar, Santosh; Iyer, Radhakrishnan; Coutinho, Evans

    2009-12-01

    A simple quantitative structure activity relationship (QSAR) approach termed local indices for similarity analysis (LISA) has been developed. In this technique, the global molecular similarity is broken up as local similarity at each grid point surrounding the molecules and is used as a QSAR descriptor. In this way, a view of the molecular sites permitting favorable and rational changes to enhance activity is obtained. The local similarity index, calculated on the basis of Petke's formula, segregates the regions into "equivalent", "favored similar", and "disfavored similar" (alternatively "favored dissimilar") potentials with respect to a reference molecule in the data set. The method has been tested on three large and diverse data sets-thrombin, glycogen phosphorylase b, and thermolysin inhibitors. The QSAR models derived using genetic algorithm incorporated partial least square analysis statistics are found to be comparable to the ones obtained by the standard three-dimensional (3D)-QSAR methods, such as comparative molecular field analysis and comparative molecular similarity indices analysis. The graphical interpretation of the LISA models is straightforward, and the outcome of the models corroborates well with literature data. The LISA models give insight into the binding mechanisms of the ligand with the enzyme and allow fine-tuning of the molecules at the local level to improve their activity.

  9. QSAR studies, synthesis and antibacterial assessment of new inhibitors against multidrug-resistant Mycobacterium tuberculosis.

    PubMed

    Kovalishyn, Vasyl; Brovarets, Volodymyr; Blagodatnyi, Volodymyr; Kopernyk, Iryna; Hodyna, Diana; Chumachenko, Svitlana; Shablykin, Oleg; Kozachenko, Oleksandr; Vovk, Myhailo; Barus, Marianna; Bratenko, Myhailo; Metelytsia, Larysa

    2016-11-08

    This paper describes Quantitative Structure-Activity Relationships (QSAR) studies using Artificial Neural Networks (ANN), synthesis and in vitro antitubercular activity of several potent compounds against H37Rv and resistant Mycobacterium tuberculosis (Mtb) strains. Eight QSAR models were built using various types of descriptors with four publicly available structurally diverse datasets, including recent data from PubChem and ChEMBL. The predictive power of the obtained QSAR models was evaluated with a cross-validation procedure, giving a q2=0.74-0.78 for regression models and overall accuracy 78.9-94.4% for classification models. The external test sets were predicted with accuracies in the range of 84.1-95.0% (for the active/inactive classifications) and q2=0.80-0.83 for regressions. The 15 synthesized compounds showed inhibitory activity against H37Rv strain whereas the compounds 1-7 were also active against resistant Mtb strain (resistant to isoniazid and rifampicin). The results indicated that compounds 1-7 could serve as promising leads for further optimization as novel antibacterial inhibitors, in particular, for the treatment of drug resistance of Mtb forms.

  10. Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery.

    PubMed

    Cruz-Monteagudo, Maykel; Schürer, Stephan; Tejera, Eduardo; Pérez-Castillo, Yunierkis; Medina-Franco, José L; Sánchez-Rodríguez, Aminael; Borges, Fernanda

    2017-03-06

    Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates.

  11. 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.

  12. The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins.

    PubMed

    Zou, Tang-Bin; He, Tai-Ping; Li, Hua-Bin; Tang, Huan-Wen; Xia, En-Qin

    2016-01-12

    Peptides derived from dietary proteins, have been reported to display significant antioxidant activity, which may exert notably beneficial effects in promoting human health and in food processing. Recently, much research has focused on the generation, separation, purification and identification of novel peptides from various protein sources. Some researchers have tried to discover the structural characteristics of antioxidant peptides in order to lessen or avoid the tedious and aimless work involving the ongoing generated peptide preparation schemes. This review aims to summarize the current knowledge on the relationship between the structural features of peptides and their antioxidant activities. The relationship between the structure of the precursor proteins and their abilities to release antioxidant fragments will also be summarized and inferred. The preparation methods and antioxidant capacity evaluation assays of peptides and a prediction scheme of quantitative structure-activity relationship (QSAR) will also be pointed out and discussed.

  13. The Discovery of Geranylgeranyltransferase-I Inhibitors with Novel Scaffolds by the Means of Quantitative Structure-Activity Relationship Modeling, Virtual Screening, and Experimental Validation

    PubMed Central

    Peterson, Yuri K.; Wang, Xiang S.; Casey, Patrick J.; Tropsha, Alexander

    2009-01-01

    Geranylgeranylation is critical to the function of several proteins including Rho, Rap1, Rac, Cdc42, and G-protein gamma subunits. Geranylgeranyltransferase type I (GGTase-I) inhibitors (GGTIs) have therapeutic potential to treat inflammation, multiple sclerosis, atherosclerosis, and many other diseases. Following our standard QSAR modeling workflow, we have developed and rigorously validated Quantitative Structure Activity Relationship (QSAR) models for 48 GGTIs using variable selection k nearest neighbor (kNN), automated lazy learning (ALL), and partial least square (PLS) methods. The QSAR models were employed for virtual screening of 9.5 million commercially available chemicals yielding 47 diverse computational hits. Seven of these compounds with novel scaffolds and high predicted GGTase-I inhibitory activities were tested in vitro, and all were found to be bona fide and selective micromolar inhibitors. Notably, these novel hits could not be identified using traditional similarity search. These data demonstrate that rigorously developed QSAR models can serve as reliable virtual screening tools. PMID:19537691

  14. Understanding hERG inhibition with QSAR models based on a one-dimensional molecular representation

    NASA Astrophysics Data System (ADS)

    Diller, David J.; Hobbs, Doug W.

    2007-07-01

    Blockage of the potassium channel encoded by the human ether-a-go-go related gene (hERG) is well understood to be the root cause of the cardio-toxicity of numerous approved and investigational drugs. As such, a cascade of in vitro and in vivo assays have been developed to filter compounds with hERG inhibitory activity. Quantitative structure activity relationship (QSAR) models are used at the very earliest part of this cascade to eliminate compounds that are likely to have this undesirable activity prior to synthesis. Here a new QSAR technique based on the one-dimensional representation is described in the context of the development of a model to predict hERG inhibition. The model is shown to perform close to the limits of the quality of the data used for model building. In order to make optimal use of the available data, a general robust mathematical scheme was developed and is described to simultaneously incorporate quantitative data, such as IC50 = 50 nM, and qualitative data, such as inactive or IC50 > 30 μM into QSAR models without discarding any experimental information.

  15. Theoretical studies on QSAR and mechanism of 2-indolinone derivatives as tubulin inhibitors

    NASA Astrophysics Data System (ADS)

    Liao, Si Yan; Qian, Li; Miao, Ti Fang; Lu, Hai Liang; Zheng, Kang Cheng

    The theoretical studies on three-dimensional quantitative structure activity relationship (3D-QSAR) and action mechanism of a series of 2-indolinone derivatives as tubulin inhibitors against human breast cancer cell line MDA-MB-231 have been carried out. The established 3D-QSAR model from the comparative molecular field analysis (CoMFA) shows not only significant statistical quality but also predictive ability, with high correlation coefficient (R2 = 0.986) and cross-validation coefficient (q2 = 0.683). In particular, the appropriate binding orientations and conformations of these 2-indolinone derivatives interacting with tubulin are located by docking study, and it is very interesting to find that the plot of the energy scores of these compounds in DOCK versus the corresponding experimental pIC50 values exhibits a considerable linear correlation. Therefore, the inhibition mechanism that 2-indolinone derivatives were regarded as tubulin inhibitors can be theoretically confirmed. Based on such an inhibition mechanism along with 3D-QSAR results, some important factors improving the activities of these compounds were discussed in detail. These factors can be summarized as follows: the H atom adopted as substituent R1, the substituent R2 with higher electropositivity and smaller bulk, the substituents R4-R6 (on the phenyl ring) with higher electropositivity and larger bulk, and so on. These results can offer useful theoretical references for understanding the action mechanism, designing more potent inhibitors, and predicting their activities prior to synthesis.

  16. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

    PubMed

    Zhu, Hao; Tropsha, Alexander; Fourches, Denis; Varnek, Alexandre; Papa, Ester; Gramatica, Paola; Oberg, Tomas; Dao, Phuong; Cherkasov, Artem; Tetko, Igor V

    2008-04-01

    Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the

  17. A DFT-based toxicity QSAR study of aromatic hydrocarbons to Vibrio fischeri: Consideration of aqueous freely dissolved concentration.

    PubMed

    Wang, Ying; Yang, Xianhai; Wang, Juying; Cong, Yi; Mu, Jingli; Jin, Fei

    2016-05-05

    In the present study, quantitative structure-activity relationship (QSAR) techniques based on toxicity mechanism and density functional theory (DFT) descriptors were adopted to develop predictive models for the toxicity of alkylated and parent aromatic hydrocarbons to Vibrio fischeri. The acute toxicity data of 17 aromatic hydrocarbons from both literature and our experimental results were used to construct QSAR models by partial least squares (PLS) analysis. With consideration of the toxicity process, the partition of aromatic hydrocarbons between water phase and lipid phase and their interaction with the target biomolecule, the optimal QSAR model was obtained by introducing aqueous freely dissolved concentration. The high statistical values of R(2) (0.956) and Q(CUM)(2) (0.942) indicated that the model has good goodness-of-fit, robustness and internal predictive power. The average molecular polarizability (α) and several selected thermodynamic parameters reflecting the intermolecular interactions played important roles in the partition of aromatic hydrocarbons between the water phase and biomembrane. Energy of the highest occupied molecular orbital (E(HOMO)) was the most influential descriptor which dominated the toxicity of aromatic hydrocarbons through the electron-transfer reaction with biomolecules. The results demonstrated that the adoption of freely dissolved concentration instead of nominal concentration was a beneficial attempt for toxicity QSAR modeling of hydrophobic organic chemicals.

  18. 2-(pyrazin-2-yloxy)acetohydrazide analogs QSAR Study: An insight into the structural basis of antimycobacterial activity.

    PubMed

    Gupta, Revathi A; Gupta, Arun K; Soni, Love K; Kaskhedikar, Satish Gopalrao

    2010-11-01

    Quantitative structure activity relationship analysis based on classical Hansch approach was adopted on reported novel series of 2-(pyrazin-2-yloxy)acetohydrazide analogs. Various types of descriptors like topological, spatial, thermodynamic, and electronic were used to derive a quantitative relationship between the antitubercular activity and structural properties. The consensus scoring function showed a significant statistics of training and test set. Coefficient of determination (r²) of consensus model and predictive squared correlation coefficient (r²(pred)) were found to be 0.889 and 0.782, respectively. The model is not only able to predict the activity of test compounds but also explained the important structural features of the molecules in a quantitative manner. The study revealed that antimycobacterium activity is predominantly explained by the molecular connectivity indices of length 6, hydrogen donor feature of the analogs, and shape factors of the substituent. The comparative investigation of antibacterial activity against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Salmonella typhi provided structural insights on how modulation of the molecular connectivity indices, energy of lowest unoccupied molecular orbital, accessible surface area, and moment of inertia of the analogs could be usefully made to optimize the antibacterial activity.

  19. QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence.

    PubMed

    Doosti, Elham; Shahlaei, Mohsen

    2015-01-01

    Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules. Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation. Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model. Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated. Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds.

  20. In-silico structure activity relationship study of toxicity endpoints by QSAR modeling (SOT)

    EPA Science Inventory

    Several thousand chemicals were tested in 700 toxicity-related in-vitro HTS bioassays through the EPA’s ToxCast and Tox21 projects. This chemical set only covers a portion of the chemical space of interest for environmental exposure, leading to a need to fill data gaps with alter...

  1. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals

    SciTech Connect

    Venkatapathy, Raghuraman Wang Chingyi; Bruce, Robert Mark; Moudgal, Chandrika

    2009-01-15

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD{sub 50}]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD{sub 50}) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD{sub 50} and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD{sub 50}s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD{sub 50} for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

  2. Development of Quantitative Structure-Activity Relationship (QSAR) Models to Predict the Carcinogenic Potency of Chemicals

    EPA Science Inventory

    Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to either: (1) identify alternative toxicity measures (shorter duration) that may be used as...

  3. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) MODELS TO PREDICT CHEMICAL TOXICITY FOR VARIOUS HEALTH ENDPOINTS

    EPA Science Inventory

    Although ranking schemes based on exposure and toxicity have been developed to aid in the prioritization of research funds for identifying chemicals of regulatory concern, there are significant gaps in the availability of experimental toxicity data for most health endpoints. Pred...

  4. 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.

  5. Towards understanding the mechanism of action of antibacterial N-alkyl-3-hydroxypyridinium salts: Biological activities, molecular modeling and QSAR studies.

    PubMed

    Dolezal, Rafael; Soukup, Ondrej; Malinak, David; Savedra, Ranylson M L; Marek, Jan; Dolezalova, Marie; Pasdiorova, Marketa; Salajkova, Sarka; Korabecny, Jan; Honegr, Jan; Ramalho, Teodorico C; Kuca, Kamil

    2016-10-04

    In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software.

  6. Augmented multivariate image analysis applied to quantitative structure-activity relationship modeling of the phytotoxicities of benzoxazinone herbicides and related compounds on problematic weeds.

    PubMed

    Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson

    2013-09-11

    Two of major weeds affecting cereal crops worldwide are Avena fatua L. (wild oat) and Lolium rigidum Gaud. (rigid ryegrass). Thus, development of new herbicides against these weeds is required; in line with this, benzoxazinones, their degradation products, and analogues have been shown to be important allelochemicals and natural herbicides. Despite earlier structure-activity studies demonstrating that hydrophobicity (log P) of aminophenoxazines correlates to phytotoxicity, our findings for a series of benzoxazinone derivatives do not show any relationship between phytotoxicity and log P nor with other two usual molecular descriptors. On the other hand, a quantitative structure-activity relationship (QSAR) analysis based on molecular graphs representing structural shape, atomic sizes, and colors to encode other atomic properties performed very accurately for the prediction of phytotoxicities of these compounds against wild oat and rigid ryegrass. Therefore, these QSAR models can be used to estimate the phytotoxicity of new congeners of benzoxazinone herbicides toward A. fatua L. and L. rigidum Gaud.

  7. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks.

    PubMed

    Prado-Prado, Francisco J; Martinez de la Vega, Octavio; Uriarte, Eugenio; Ubeira, Florencio M; Chou, Kuo-Chen; González-Díaz, Humberto

    2009-01-15

    One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the

  8. Approach on quantitative structure-activity relationship for design of a pH neutral carrier containing tertiary amino group.

    PubMed

    Cao, Zhong; Gong, Fu-Chun; Li, He-Ping; Xiao, Zhong-Liang; Long, Shu; Zhang, Ling; Peng, San-Jun

    2007-01-02

    The quantitative structure-activity relationship (QSAR) for neutral carriers used to prepare hydrogen ion sensors has been studied. A series of synthesized carrier compounds were taken as the training set. Five molecular structure parameters of the compounds were calculated by using CNDO/2 algorithm and used as feature variables in constructing QSAR model. The lower and upper limits of the linear pH response range were taken as the activity measure. The corresponding model equations were derived from the stepwise regression procedure. With the established QSAR model, a new pH carrier, (4-hydroxybenzyl) didodecylamine (XIII) was proposed and synthesized. The PVC membrane pH electrode based on carrier XIII with a wide pH linear response range of 2.0-12.5 was prepared. Having a theoretical Nernstian response slope of 57.2+/-0.3 mV/pH (n=5 at 25 degrees C) without a super-Nernstian phenomenon, the sensor had low resistance, short response time, high selectivity and good reproducibility. Moreover, the sensor was successfully applied to detecting the pH value of serum samples.

  9. QSAR: an in silico approach for predicting the partitioning of pesticides into breast milk.

    PubMed

    Agatonovic-Kustrin, Suezana; Morton, David W; Celebic, D

    2013-03-01

    The aim of this study was to develop an in silico Quantitative Structure Activity Relationship (QSAR) model capable of predicting partitioning of pesticides into breast milk from their respective chemical structures. A large data set of 190 diverse compounds, including drugs and their active metabolites (87%), and pesticides (13%) with experimentally derived milk/plasma (M/P) ratios taken from the literature, was used to train, test and validate a predictive model. Each compound was encoded with 65 calculated chemical structure descriptors. Sensitivity analysis was then used to select a subset of the descriptors that best describe the transfer of pesticides into breast milk and Artificial neural networks modeling was applied to correlate selected descriptors (inputs) with the M/P ratio (output) in order to develop a predictive QSAR. The developed QSAR model included 26 molecular descriptors related to the molecular size, polarity and hydrogen binding capacity. Together with aromatic rings, these descriptors account for molecule's size and hydrophobic interaction capabilities. The average correlation for the final model (incorporating training, testing, and validation) was 0.85. The developed model provides a useful method for predicting the M/P ratios of pesticides from just a sketch of their respective molecular structures. However, these predictions should only be used to assist in the evaluation of risk in conjunction with an assessment of the infant's response to a given drug/pesticide.

  10. QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

    PubMed

    Papa, E; Kovarich, S; Gramatica, P

    2013-01-01

    The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

  11. Design, synthesis and 3D-QSAR study of cytotoxic flavonoid derivatives.

    PubMed

    Ou, Lili; Han, Shuang; Ding, Wenbo; Chen, Zhe; Ye, Ziqi; Yang, Hongyu; Zhang, Goulin; Lou, Yijia; Chen, Jian-Zhong; Yu, Yongping

    2011-08-01

    Three series of flavonoid derivatives were designed and synthesized. All synthesized compounds were evaluated for cytotoxic activities against five human cancer cell lines, including K562, PC-3, MCF-7, A549, and HO8910. Among the compounds tested, compound 9 d exhibited the most potent cytotoxic activity with IC(50) values of 2.76-6.98 μM. Further comparative molecular field analysis was performed to conduct a 3D quantitative structure-activity relationship study. The generated 3D-QSAR model could be used for further rational design of novel flavonoid analogs as highly potent cytotoxic agents.

  12. Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

    PubMed

    Ivanciuc, Ovidiu

    2013-06-01

    Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

  13. Structure-Thermodynamics-Antioxidant Activity Relationships of Selected Natural Phenolic Acids and Derivatives: An Experimental and Theoretical Evaluation

    PubMed Central

    Zheng, Jie; Liang, Guizhao

    2015-01-01

    Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media. PMID:25803685

  14. Structure-thermodynamics-antioxidant activity relationships of selected natural phenolic acids and derivatives: an experimental and theoretical evaluation.

    PubMed

    Chen, Yuzhen; Xiao, Huizhi; Zheng, Jie; Liang, Guizhao

    2015-01-01

    Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media.

  15. Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship.

    PubMed

    Navarro-Retamal, Carlos; Caballero, Julio

    2016-01-01

    In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure-activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities.

  16. Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship

    PubMed Central

    Navarro-Retamal, Carlos

    2016-01-01

    In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure–activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities. PMID:27517610

  17. Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3β.

    PubMed

    Quesada-Romero, Luisa; Caballero, Julio

    2014-02-01

    The binding modes of 42 oxadiazole derivates inside glycogen synthase kinase 3 beta (GSK3β were determined using docking experiments; thus, the preferred active conformations of these inhibitors are proposed. We found that these compounds adopt a scorpion-shaped conformation and they accept a hydrogen bond (HB) from the residue Val135 of the GSK3β ATP-binding site hinge region. In addition, quantitative structure-activity relationship (QSAR) models were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. In a first approach, three-dimensional (3D) vectors were calculated using docking conformations and, by using multiple-linear regression, we assessed that GETAWAY vectors were able to describe the reported biological activities. In other QSAR approach, SMILES-based optimal descriptors were calculated. The best model included three-SMILES elements SSSβ leading to the identification of key molecular features that contribute to a high GSK3β inhibitory activity.

  18. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    SciTech Connect

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  19. QSID Tool: a new three-dimensional QSAR environmental tool

    NASA Astrophysics Data System (ADS)

    Park, Dong Sun; Kim, Jae Min; Lee, Young Bok; Ahn, Chang Ho

    2008-12-01

    QSID Tool (Quantitative structure-activity relationship tool for Innovative Discovery) was developed to provide an easy-to-use, robust and high quality environmental tool for 3D QSAR. Predictive models developed with QSID Tool can accelerate the discovery of lead compounds by enabling researchers to formulate and test hypotheses for optimizing efficacy and increasing drug safety and bioavailability early in the process of drug discovery. QSID Tool was evaluated by comparison with SYBYL® using two different datasets derived from the inhibitors of Trypsin (Böhm et al., J Med Chem 42:458, 1999) and p38-MAPK (Liverton et al., J Med Chem 42:2180, 1999; Romeiro et al., J Comput Aided Mol Des 19:385, 2005; Romeiro et al., J Mol Model 12:855, 2006). The results suggest that QSID Tool is a useful model for the prediction of new analogue activities.

  20. Development and validation of quantitative structure-activity relationship models for compounds acting on serotoninergic receptors.

    PubMed

    Zydek, Grażyna; Brzezińska, Elżbieta

    2012-01-01

    A quantitative structure-activity relationship (QSAR) study has been made on 20 compounds with serotonin (5-HT) receptor affinity. Thin-layer chromatographic (TLC) data and physicochemical parameters were applied in this study. RP2 TLC 60F(254) plates (silanized) impregnated with solutions of propionic acid, ethylbenzene, 4-ethylphenol, and propionamide (used as analogues of the key receptor amino acids) and their mixtures (denoted as S1-S7 biochromatographic models) were used in two developing phases as a model of drug-5-HT receptor interaction. The semiempirical method AM1 (HyperChem v. 7.0 program) and ACD/Labs v. 8.0 program were employed to calculate a set of physicochemical parameters for the investigated compounds. Correlation and multiple linear regression analysis were used to search for the best QSAR equations. The correlations obtained for the compounds studied represent their interactions with the proposed biochromatographic models. The good multivariate relationships (R(2) = 0.78-0.84) obtained by means of regression analysis can be used for predicting the quantitative effect of biological activity of different compounds with 5-HT receptor affinity. "Leave-one-out" (LOO) and "leave-N-out" (LNO) cross-validation methods were used to judge the predictive power of final regression equations.

  1. Pharmacophore modeling, 3D-QSAR and molecular docking studies of benzimidazole derivatives as potential FXR agonists.

    PubMed

    Sindhu, Thangaraj; Srinivasan, Pappu

    2014-08-01

    Farnesoid X receptor (FXR) is a potential therapeutic target for the treatment of diabetes mellitus. Atom-based three-dimensional quantitative structure activity relationship (3D-QSAR) models were developed for a series of 48 benzimidazole-based agonists of FXR. A total of five pharmacophore hypotheses were generated based on the survival score to build QSAR models. HHHRR was considered as a best model that consisted of three hydrophobic features (H) and two aromatic rings (R). The best hypothesis, HHHRR yielded a 3D-QSAR model with good statistical value (R(2)) of 0.8974 for a training set of 39 compounds and also showed good predictive power with correlation coefficient (Q(2)) of 0.7559 for a test set of nine compounds. Furthermore, molecular docking simulation was performed to understand the binding affinity of 48 benzimidazole-based compounds against the active site of human FXR protein. Docking results revealed that both the most active and least active compounds showed similar binding mode to the experimentally observed binding mode of co-crystallized ligand. The generated 3D contour maps revealed the structure activity relationship of the compounds. Substitution effects at different positions of benzimidazole derivatives would lead to the discovery of new agonists against human FXR protein.

  2. 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.

  3. Variable selection based QSAR modeling on Bisphenylbenzimidazole as Inhibitor of HIV-1 reverse transcriptase.

    PubMed

    Kumar, Surendra; Tiwari, Meena

    2013-11-01

    The emergence of mutant virus in drug therapy for HIV-1 infection has steadily risen in the last decade. Inhibition of reverse transcriptase enzyme has emerged as a novel target for the treatment of HIV infection. The aim to decipher the structural features that interact with receptor, we report a quantitative structure activity relationship (QSAR) study on a dataset of thirty seven compounds belonging to bisphenylbenzimidazoles (BPBIs) as reverse transcriptase inhibitors using enhanced replacement method (ERM), stepwise multiple linear regression (Stepwise-MLR) and genetic function approximation (GFA) method for selecting a subset of relevant descriptors, developing the best multiple linear regression model and defining the QSAR model applicability domain boundaries. The enhanced replacement method was found to give better results r²=0.8542, Q²(loo) = 0.7917, r²pred = 0.7812) at five variables as compared to stepwise MLR and GFA method, evidenced by internal and external validation parameters. The modified r² (r²m) of the training set, test set and whole data set were calculated and are in agreement with the enhanced replacement method. The results of QSAR study rationalize the structural requirement for optimum binding of ligands. The developed QSAR model shows that hydrophobicity, flexibility, three dimensional surface area, volume and shape of molecule are important parameters to be considered for designing new compounds and to decipher reverse transcriptase enzyme inhibition activity of these compounds at molecular level. The applicability domain was defined to find the similar analogs with better prediction power.

  4. Molecular modeling of histamine H3 receptor and QSAR studies on arylbenzofuran derived H3 antagonists.

    PubMed

    Dastmalchi, Siavoush; Hamzeh-Mivehroud, Maryam; Ghafourian, Taravat; Hamzeiy, Hossain

    2008-01-01

    Histamine H3 receptors are presynaptic autoreceptors found in both central and peripheral nervous systems of many species. The central effects of these receptors suggest a potential therapeutic role for their antagonists in treatment of several neurological disorders such as epilepsy, schizophrenia, Alzheimer's and Parkinson's diseases. The purpose of this study was to identify the structural requirements for H3 antagonistic activity via quantitative structure-activity relationship (QSAR) studies and receptor modeling/docking techniques. A combination of partial least squares (PLS) and genetic algorithm (GA) was used in the QSAR approach to select the structural descriptors relevant to the receptor binding affinity of a series of 58 H3 antagonists. The descriptors were selected out of a pool of >1000 descriptors calculated by DRAGON, Hyperchem and ACD labs suite of programs. The resulting QSAR models for rat and human H3 binding affinities were validated using different strategies. QSAR models generated in the current work suggested the role of charge transfer interactions in the ligand-receptor interaction verified using the molecular modeling of the receptor and docking two antagonists to the binding site. The 3D model of human H3 receptor was built based on bovine rhodopsin structure and evaluated by molecular dynamics (MD) simulation in a mixed water-vacuum-water environment. The results were indicative of the stability of the model relating the observed structural changes during the MD simulation to the suggested ligand-receptor interactions. The results of this investigation are expected to be useful in the process of design and development of new potent H3 receptor antagonists.

  5. QSAR study for the soybean 15-lipoxygenase inhibitory activity of organosulfur compounds derived from the essential oil of garlic.

    PubMed

    Camargo, Alejandra B; Marchevsky, Eduardo; Luco, Juan M

    2007-04-18

    In this study, multiple linear regression (MLR) and partial least-squares (PLS) techniques were used for modeling the soybean 15-lipoxygenase inhibitory activity of a varied group of mono-, di-, and trisulfides derived from the essential oil of garlic. The structures of the compounds under study were characterized by means of calculated physicochemical parameters and several nonempirical descriptors, such as topological, geometrical, and quantum chemical indices. The results obtained indicate that the inhibitory activity is strongly dependent on the ability of the compounds to participate in dispersive interactions with the enzyme, as expressed by the solvent-accessible surface area (SASA) and the average distance/distance degree descriptor (ADDD) index. On the other hand, the high contribution of the lowest unoccupied molecular orbit term (LUMO) in the PLS models derived for the di- and trisulfides suggests that the solute's electron-acceptor capacity plays a fundamental role in the inhibitory activity exhibited for these compounds. Finally, the geometric features as expressed by the shape parameters included in the models indicate a low but not negligible positive contribution of molecular linearity in the enzyme-inhibitor binding. In summary, the developed quantitative structure-activity relationship approach successfully accounts for the potencies of organosulfur compounds acting on soybean 15-lipoxygenase and thereby offers both a guide for the synthesis of new compounds and a hypothesis for the molecular basis of their activity.

  6. Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna.

    PubMed

    Toropova, Alla P; Toropov, Andrey A; Veselinović, Aleksandar M; Veselinović, Jovana B; Leszczynska, Danuta; Leszczynski, Jerzy

    2016-11-01

    Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r(2)  = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.

  7. The interaction of flavonoid-lysozyme and the relationship between molecular structure of flavonoids and their binding activity to lysozyme.

    PubMed

    Yang, Ran; Yu, Lanlan; Zeng, Huajin; Liang, Ruiling; Chen, Xiaolan; Qu, Lingbo

    2012-11-01

    In this work, the interactions of twelve structurally different flavonoids with Lysozyme (Lys) were studied by fluorescence quenching method. The interaction mechanism and binding properties were investigated. It was found that the binding capacities of flavonoids to Lys were highly depend on the number and position of hydrogen, the kind and position of glycosyl. To explore the selectivity of the bindings of flavonoids with Lys, the structure descriptors of the flavonoids were calculated under QSAR software package of Cerius2, the quantitative relationship between the structures of flavonoids and their binding activities to Lys (QSAR) was performed through genetic function approximation (GFA) regression analysis. The QSAR regression equation was K(A) = 37850.460 + 1630.01Dipole +3038.330HD-171.795MR. (r = 0.858, r(CV)(2) = 0.444, F((11,3)) = 7.48), where K(A) is binding constants, Dipole, HD and MR was dipole moment, number of hydrogen-bond donor and molecular refractivity, respectively. The obtained results make us understand better how the molecular structures influencing their binding to protein which may open up new avenues for the design of the most suitable flavonoids derivatives with structure variants.

  8. QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants.

    PubMed

    Papa, Ester; Kovarich, Simona; Gramatica, Paola

    2010-05-17

    In the European Union REACH regulation, the chemicals with particularly harmful behaviors, such as endocrine disruptors (EDs), are subject to authorization, and the identification of safer alternatives to these chemicals is required. In this context, the use of quantitative structure-activity relationships (QSAR) becomes particularly useful to fill the data gap due to the very small number of experimental data available to characterize the environmental and toxicological profiles of new and emerging pollutants with ED behavior such as brominated flame retardants (BFRs). In this study, different QSAR models were developed on different responses of endocrine disruption measured for several BFRs. The multiple linear regression approach was applied to a variety of theoretical molecular descriptors, and the best models, which were identified from all of the possible combinations of the structural variables, were internally validated for their performance using the leave-one-out (Q(LOO)(2) = 73-91%) procedure and scrambling of the responses. External validation was provided, when possible, by splitting the data sets in training and test sets (range of Q(EXT)(2) = 76-90%), which confirmed the predictive ability of the proposed equations. These models, which were developed according to the principles defined by the Organization for Economic Co-operation and Development to improve the regulatory acceptance of QSARs, represent a simple tool for the screening and characterization of BFRs.

  9. QSAR model as a random event: A case of rat toxicity.

    PubMed

    Toropova, Alla P; Toropov, Andrey A; Benfenati, Emilio; Leszczynska, Danuta; Leszczynski, Jerzy

    2015-03-15

    Quantitative structure-property/activity relationships (QSPRs/QSARs) can be used to predict physicochemical and/or biochemical behavior of substances which were not studied experimentally. Typically predicted values for chemicals in the training set are accurate since they were used to build the model. QSPR/QSAR models must be validated before they are used in practice. Unfortunately, the majority of the suggested approaches of the validation of QSPR/QSAR models are based on consideration of geometrical features of clusters of data points in the plot of experimental versus calculated values of an endpoint. We believe these geometrical criteria can be more useful if they are analyzed for several splits into the training and test sets. In this way, one can estimate the reproducibility of the model with various splits and better evaluate model reliability. The probability of the correct prediction of an endpoint for external validation set (in the series of the above-mentioned splits) can provide an useful way to evaluate the domain of applicability of the model.

  10. QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

    PubMed Central

    Artemenko, A.G.; Muratov, E. N.; Kuz’min, V.E.; Muratov, N.N.; Varlamova, E.V.; Kuz'mina, A.V.; Gorb, L. G.; Golius, A.; Hill, F.C.; Leszczynski, J.; Tropsha, A.

    2012-01-01

    The Hierarchical Technology for Quantitative Structure - Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC50) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. Then, Partial Least Squares (PLS) statistical approach was used for the development of 2D QSAR models. Validated PLS models were explored to (i) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (ii) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; (iii) analyze the role of various physical-chemical factors responsible for compounds’ toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (R2ext=0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modeling and 76% for external set). PMID:21714735

  11. 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

  12. Molecular design and QSARs/QSPRs with molecular descriptors family.

    PubMed

    Bolboacă, Sorana D; Jäntschi, Lorentz; Diudea, Mircea V

    2013-06-01

    The aim of the present paper is to present the methodology of the molecular descriptors family (MDF) as an integrative tool in molecular modeling and its abilities as a multivariate QSAR/QSPR modeling tool. An algorithm for extracting useful information from the topological and geometrical representation of chemical compounds was developed and integrated to calculate MDF members. The MDF methodology was implemented and the software is available online (http://l.academicdirect.org/Chemistry/SARs/MDF_SARs/). This integrative tool was developed in order to maximize performance, functionality, efficiency and portability. The MDF methodology is able to provide reliable and valid multiple linear regression models. Furthermore, in many cases, the MDF models were better than the published results in the literature in terms of correlation coefficients (statistically significant Steiger's Z test at a significance level of 5%) and/or in terms of values of information criteria and Kubinyi function. The MDF methodology developed and implemented as a platform for investigating and characterizing quantitative relationships between the chemical structure and the activity/property of active compounds was used on more than 50 study cases. In almost all cases, the methodology allowed obtaining of QSAR/QSPR models improved in explanatory power of structure-activity and structure-property relationships. The algorithms applied in the computation of geometric and topological descriptors (useful in modeling physicochemical or biological properties of molecules) and those used in searching for reliable and valid multiple linear regression models certain enrich the pool of low-cost low-time drug design tools.

  13. Structure-Activity Relationship Analysis of the Thermal Stabilities of Nitroaromatic Compounds Following Different Decomposition Mechanisms.

    PubMed

    Li, Jiazhong; Liu, Huanxiang; Huo, Xing; Gramatica, Paola

    2013-02-01

    The decomposition behavior of energetic materials is very important for the safety problems concerning their production, transportation, use and storage, because molecular decomposition is intimately connected to their explosive properties. Nitroaromatic compounds, particularly nitrobenzene derivatives, are often considered as prototypical energetic molecules, and some of them are commonly used as high explosives. Quantitative structure-activity relationship (QSAR) represents a potential tool for predicting the thermal stability properties of energetic materials. But it is reported that constructing general reliable models to predict their stability and their potential explosive properties is a very difficult task. In this work, we make our efforts to investigate the relationship between the molecular structures and corresponding thermal stabilities of 77 nitrobenzene derivatives with various substituent functional groups (in ortho, meta and/or para positions). The proposed best MLR model, developed by the new software QSARINS, based on Genetic Algorithm for variable selection and with various validation tools, is robust, stable and predictive with R(2) of 0.86, QLOO (2) of 0.79 and CCC of 0.90. The results indicated that, though difficult, it is possible to build predictive, externally validated QSAR models to estimate the thermal stability of nitroaromatic compounds.

  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. QSAR models for the removal of organic micropollutants in four different river water matrices.

    PubMed

    Sudhakaran, Sairam; Calvin, James; Amy, Gary L

    2012-04-01

    Ozonation is an advanced water treatment process used to remove organic micropollutants (OMPs) such as pharmaceuticals and personal care products (PPCPs). In this study, Quantitative Structure Activity Relationship (QSAR) models, for ozonation and advanced oxidation process (AOP), were developed with percent-removal of OMPs by ozonation as the criterion variable. The models focused on PPCPs and pesticides elimination in bench-scale studies done within natural water matrices: Colorado River, Passaic River, Ohio River and Suwannee synthetic water. The OMPs removal for the different water matrices varied depending on the water quality conditions such as pH, DOC, alkalinity. The molecular descriptors used to define the OMPs physico-chemical properties range from one-dimensional (atom counts) to three-dimensional (quantum-chemical). Based on a statistical modeling approach using more than 40 molecular descriptors as predictors, descriptors influencing ozonation/AOP were chosen for inclusion in the QSAR models. The modeling approach was based on multiple linear regression (MLR). Also, a global model based on neural networks was created, compiling OMPs from all the four river water matrices. The chemically relevant molecular descriptors involved in the QSAR models were: energy difference between lowest unoccupied and highest occupied molecular orbital (E(LUMO)-E(HOMO)), electron-affinity (EA), number of halogen atoms (#X), number of ring atoms (#ring atoms), weakly polar component of the solvent accessible surface area (WPSA) and oxygen to carbon ratio (O/C). All the QSAR models resulted in a goodness-of-fit, R(2), greater than 0.8. Internal and external validations were performed on the models.

  16. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.

    PubMed

    Liu, Ruifeng; Schyman, Patric; Wallqvist, Anders

    2015-08-24

    To lower the possibility of late-stage failures in the drug development process, an up-front assessment of absorption, distribution, metabolism, elimination, and toxicity is commonly implemented through a battery of in silico and in vitro assays. As in vitro data is accumulated, in silico quantitative structure-activity relationship (QSAR) models can be trained and used to assess compounds even before they are synthesized. Even though it is generally recognized that QSAR model performance deteriorates over time, rigorous independent studies of model performance deterioration is typically hindered by the lack of publicly available large data sets of structurally diverse compounds. Here, we investigated predictive properties of QSAR models derived from an assembly of publicly available human liver microsomal (HLM) stability data using variable nearest neighbor (v-NN) and random forest (RF) methods. In particular, we evaluated the degree of time-dependent model performance deterioration. Our results show that when evaluated by 10-fold cross-validation with all available HLM data randomly distributed among 10 equal-sized validation groups, we achieved high-quality model performance from both machine-learning methods. However, when we developed HLM models based on when the data appeared and tried to predict data published later, we found that neither method produced predictive models and that their applicability was dramatically reduced. On the other hand, when a small percentage of randomly selected compounds from data published later were included in the training set, performance of both machine-learning methods improved significantly. The implication is that 1) QSAR model quality should be analyzed in a time-dependent manner to assess their true predictive power and 2) it is imperative to retrain models with any up-to-date experimental data to ensure maximum applicability.

  17. 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

  18. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA.

    PubMed

    Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2015-01-01

    Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure-activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes.

  19. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

    PubMed Central

    Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2015-01-01

    Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure–activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes. PMID:26599107

  20. Toxicity of aryl- and benzylhalides to Daphnia magna and classification of their mode of action based on quantitative structure-activity relationship

    SciTech Connect

    Marchini, S.; Passerini, L.; Hoglund, M.D.; Pino, A.; Nendza, M.

    1999-12-01

    The acute toxicity of aryl- and benzylhalides to Daphnia magna was investigated to test the validity of existing classification schemes for chemicals by mode of action, mainly based on fish studies, and the applicability of predictive quantitative structure-activity relationship (QSAR) models. Halobenzenes and halotoluenes are generally agreed to be unambiguous baseline toxicants (class 1) with the major exception of the benzylic structures, which are reactive in fish tests (class 3). Eighty-nine percent of the arylhalides tested in this study match a log P{sub ow}-dependent QSAR, including fluorinated, chlorinated, brominated, and iodinated derivatives, thereby confirming the validity of the baseline models also for variously halogenated compounds (other than only-chloro compounds). The toxicities of the benzylhalides relative to baseline QSARs clearly indicate that these compounds belong to two classes of mode of action, i.e., they either act as narcotic toxicants (class 1) or reveal excess toxicity due to unspecific reactivity (class 3). On some occasions, the assignment to the two classes of F. magna deviates from the structural rules derived from fish, i.e., iodinated compounds as well as {alpha},{alpha}-Cl{sub 2}-toluene's lack reactive excess toxicity but behave as nonpolar nonspecific toxicants. The QSARs derived during this study reveal lower slopes and higher intercepts than typical baseline models and, together with the analysis of mixture toxicity studies, behavioral studies, and critical body burden, advocate the hypothesis that there are several different ways to produce baseline toxicity. Most halobenzenes and halotoluenes are actually baseline chemicals with some extra reactivity and as such form a subgroup, whose limits still have to be defined. Different primary sites of action could explain why the chemicals are discriminated by different classification systems, but still they must have some rate-limiting interaction in common as they fit the

  1. Molecular docking and 3D-QSAR studies on the binding mechanism of statine-based peptidomimetics with beta-secretase.

    PubMed

    Zuo, Zhili; Luo, Xiaomin; Zhu, Weiliang; Shen, Jianhua; Shen, Xu; Jiang, Hualiang; Chen, Kaixian

    2005-03-15

    beta-Secretase is an important protease in the pathogenesis of Alzheimer's disease. Some statine-based peptidomimetics show inhibitory activities to the beta-secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on these analogues were performed. The Lamarckian Genetic Algorithm (LGA) was applied to locate the binding orientations and conformations of the peptidomimetics with the beta-secretase. A good correlation between the calculated binding free energies and the experimental inhibitory activities suggests that the identified binding conformations of these potential inhibitors are reliable. Based on the binding conformations, highly predictive 3D-QSAR models were developed with q(2) values of 0.582 and 0.622 for CoMFA and CoMSIA, respectively. The predictive abilities of these models were validated by some compounds that were not included in the training set. Furthermore, the 3D-QSAR models were mapped back to the binding site of the beta-secretase, to get a better understanding of vital interactions between the statine-based peptidomimetics and the protease. Both the CoMFA and the CoMSIA field distributions are in well agreement with the structural characteristics of the binding groove of the beta-secretase. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction would be useful in developing new drug leads against Alzheimer's disease.

  2. Structure- and ligand-based structure-activity relationships for a series of inhibitors of aldolase.

    PubMed

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2012-12-01

    Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r²=0.98 and q²=0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.

  3. Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS.

    PubMed

    de Campos, Luana Janaína; de Melo, Eduardo Borges

    2014-11-01

    In the present study, we performed a multivariate quantitative structure-activity relationship (QSAR) analysis of 52 prodiginines with antimalarial activity. Variable selection was based on the genetic algorithm (GA) and ordered predictor selection (OPS) approaches, and the models were built using the multiple linear regression (MLR) and partial least squares (PLS) regression methods. The leave-N-out crossvalidation and y-randomization tests showed that the models were robust and free from chance correlation. The mechanistic interpretation of the results was supported by earlier findings. In addition, the comparison of our models with those previously described indicated that the OPS/PLS-based model had a higher quality of external prediction. Thus, this study provides a comprehensive approach to the evaluation of the antimalarial activity of prodiginines, which may be used as a support tool in designing new therapeutic agents for malaria.

  4. QSAR of Tryptanthrin Analogs via Tunneling Barrier Height Imaging

    NASA Astrophysics Data System (ADS)

    Sriraman, Krishnan

    A new class of potential therapeutic agents, namely indolo[2,1-b]quinazolin-6,12- dione (tryptanthrin), and its analogues, have generated interest due to their broad spectrum of activity against a variety of pathogenic organisms. Little is known about the mechanism of action of tryptanthrins at the cellular and molecular levels. One method that has been employed to understand mechanisms of action and predict biological activities is quantitative structure activity relationship (QSAR). Since previous tryptanthrin studies could not clearly identify the pharmacophore, it was proposed to measure barrier height (BH) energy values for preparing a QSAR vs. IC50 values from the literature. The BH energy values were measured using barrier height spectroscopy which is performed using a scanning tunneling microscope (STM). Topography images (7 x 7 nm size) for tryptanthrin and three of its analogs namely 8-fluorotryptanthrin, 4-aza-8-fluorotryptanthrin, and 4-aza-8-chlorotryptanthrin were collected. Both HOMO and LUMO were collected at an applied bias of +/- 0.8 V and 0.1 nA. Excellent positive bias images (LUMO) of tryptanthrin were collected in which individual molecules and their lobes could be clearly recognized. A comparison with the density functional theory (DFT) calculated image of the LUMO resulted in an excellent match. An interesting outcome of the tryptanthrin LUMO imaging was the arrangement of molecules (parallel alignments) in the image which was explained by considering the intermolecular forces. Excellent BH images with sub-molecular resolution for 4-aza-8-fluorotryptanthrin were observed. BH values were calculated for each of the various lobes in the molecule from the BH image. Preliminary QSAR training sets were constructed using literature values of IC50 for W-2 and D-6 strains of Plasmodium falciparum as well as Leishmanai donovani versus average measured molecular barrier heights. The correlations were found to be fair for all the three pathogens. The

  5. Designing a Quantitative Structure-Activity Relationship for the ...

    EPA Pesticide Factsheets

    Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While intrinsic metabolic clearance rates have a strong impact on toxicokinetics, limited data is available for environmentally relevant chemicals including nearly 8000 chemicals tested for in vitro bioactivity in the Tox21 program. To address this gap, a quantitative structure-activity relationship (QSAR) for intrinsic metabolic clearance rate was developed to offer reliable in silico predictions for a diverse array of chemicals. Models were constructed with curated in vitro assay data for both pharmaceutical-like chemicals (ChEMBL database) and environmentally relevant chemicals (ToxCast screening) from human liver microsomes (2176 from ChEMBL) and human hepatocytes (757 from ChEMBL and 332 from ToxCast). Due to variability in the experimental data, a binned approach was utilized to classify metabolic rates. Machine learning algorithms, such as random forest and k-nearest neighbor, were coupled with open source molecular descriptors and fingerprints to provide reasonable estimates of intrinsic metabolic clearance rates. Applicability domains defined the optimal chemical space for predictions, which covered environmental chemicals well. A reduced set of informative descriptors (including relative charge and lipophilicity) and a mixed training set of pharmaceuticals and environmentally relevant chemicals provided the best intr

  6. Structure activity relationships to assess new chemicals under TSCA

    SciTech Connect

    Auletta, A.E.

    1990-12-31

    Under Section 5 of the Toxic Substances Control Act (TSCA), manufacturers must notify the US Environmental Protection Agency (EPA) 90 days before manufacturing, processing, or importing a new chemical substance. This is referred to as a premanufacture notice (PMN). The PMN must contain certain information including chemical identity, production volume, proposed uses, estimates of exposure and release, and any health or environmental test data that are available to the submitter. Because there is no explicit statutory authority that requires testing of new chemicals prior to their entry into the market, most PMNs are submitted with little or no data. As a result, EPA has developed special techniques for hazard assessment of PMN chemicals. These include (1) evaluation of available data on the chemical itself, (2) evaluation of data on analogues of the PMN, or evaluation of data on metabolites or analogues of metabolites of the PMN, (3) use of quantitative structure activity relationships (QSARs), and (4) knowledge and judgement of scientific assessors in the interpretation and integration of the information developed in the course of the assessment. This approach to evaluating potential hazards of new chemicals is used to identify those that are most in need of addition review of further testing. It should not be viewed as a replacement for testing. 4 tabs.

  7. 2D, 3D-QSAR and docking studies of 1,2,3-thiadiazole thioacetanilides analogues as potent HIV-1 non-nucleoside reverse transcriptase inhibitors

    PubMed Central

    2012-01-01

    Background The discovery of clinically relevant inhibitors of HIV-RT for antiviral therapy has proven to be a challenging task. To identify novel and potent HIV-RT inhibitors, the quantitative structure–activity relationship (QSAR) approach became very useful and largely widespread technique forligand-based drug design. Methods We perform the two- and three-dimensional (2D and 3D) QSAR studies of a series of 1,2,3-thiadiazole thioacetanilides analogues to elucidate the structural properties required for HIV-RT inhibitory activity. Results The 2D-QSAR studies were performed using multiple linear regression method, giving r2 = 0.97 and q2 = 0.94. The 3D-QSAR studies were performed using the stepwise variable selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q2 = 0.89 and a non-cross-validated correlation coefficient r2 = 0.97 were obtained. Docking analysis suggests that the new series have comparable binding affinity with the standard compounds. Conclusions This approach showed that hydrophobic and electrostatic effects dominantly determine binding affinities which will further useful for development of new NNRTIs. PMID:22691718

  8. Cytotoxic lanostane-type triterpenoids from the fruiting bodies of Ganoderma lucidum and their structure-activity relationships.

    PubMed

    Chen, Shaodan; Li, Xiangmin; Yong, Tianqiao; Wang, Zhanggen; Su, Jiyan; Jiao, Chunwei; Xie, Yizhen; Yang, Burton B

    2017-02-07

    We conducted a study of Ganoderma lucidum metabolites and isolated 35 lanostane-type triterpenoids, including 5 new ganoderols (1-5). By spectroscopy, we compared the structures of these compounds with known related compounds in this group. All of the isolated compounds were assayed for their effect against the human breast carcinoma cell line MDA-MB-231 and hepatocellular carcinoma cell line HepG2. Corresponding three-dimensional quantitative structure-activity relationship (3D-QSAR) models were built and analyzed using Discovery Studio. These results provide further evidence for anti-cancer constituents within Ganoderma lucidum, and may provide a theoretical foundation for designing novel therapeutic compounds.

  9. Toxicity on the luminescent bacterium Vibrio fischeri (Beijerinck). I: QSAR equation for narcotics and polar narcotics.

    PubMed

    Vighi, Marco; Migliorati, Sonia; Monti, Gianna Serafina

    2009-01-01

    Toxicity data on chemicals, supposed to have a narcotic or polar narcotic toxicological mode of action, have been produced on the luminescent bacterium Vibrio fischeri using the Microtox test procedure. Advanced statistical methods have been used to calculate statistically sound values for ecotoxicological endpoints. Simple quantitative structure activity relationship (QSAR) equations were developed for narcotics and polar narcotics. These equations were compared with those proposed by the European Technical Guidance Document on Risk Assessment for other aquatic organisms (algae, Daphnia, and fish). Similarities and differences are discussed. The need for including the bacterial component in the ecotoxicological risk assessment for aquatic ecosystems is highlighted.

  10. 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.

  11. Virulence factor activity relationships (VFARs): a bioinformatics perspective.

    PubMed

    Waseem, Hassan; Williams, Maggie R; Stedtfeld, Tiffany; Chai, Benli; Stedtfeld, Robert D; Cole, James R; Tiedje, James M; Hashsham, Syed A

    2017-03-06

    Virulence factor activity relationships (VFARs) - a concept loosely based on quantitative structure-activity relationships (QSARs) for chemicals was proposed as a predictive tool for ranking risks due to microorganisms relevant to water safety. A rapid increase in sequencing capabilities and bioinformatics tools has significantly increased the potential for VFAR-based analyses. This review summarizes more than 20 bioinformatics databases and tools, developed over the last decade, along with their virulence and antimicrobial resistance prediction capabilities. With the number of bacterial whole genome sequences exceeding 241 000 and metagenomic analysis projects exceeding 13 000 and the ability to add additional genome sequences for few hundred dollars, it is evident that further development of VFARs is not limited by the availability of information at least at the genomic level. However, additional information related to co-occurrence, treatment response, modulation of virulence due to environmental and other factors, and economic impact must be gathered and incorporated in a manner that also addresses the associated uncertainties. Of the bioinformatics tools, a majority are either designed exclusively for virulence/resistance determination or equipped with a dedicated module. The remaining have the potential to be employed for evaluating virulence. This review focusing broadly on omics technologies and tools supports the notion that these tools are now sufficiently developed to allow the application of VFAR approaches combined with additional engineering and economic analyses to rank and prioritize organisms important to a given niche. Knowledge gaps do exist but can be filled with focused experimental and theoretical analyses that were unimaginable a decade ago. Further developments should consider the integration of the measurement of activity, risk, and uncertainty to improve the current capabilities.

  12. Quantitative structure-activity relationships for nasal pungency thresholds of volatile organic compounds.

    PubMed

    Hau, K M; Connell, D W; Richardson, B J

    1999-01-01

    A model was developed for describing the triggering of nasal pungency in humans, based on the partition of volatile organic compounds (VOCs) between the air phase and the biophase. Two partition parameters are used in the model: the water-air partition coefficient and the octanol-water partition coefficient. The model was validated using data from the literature, principally on alcohols, acetates and ketones. The model suggests that all test compounds, regardless of their chemical functional groups, bind to a common receptor site within the hydrophobic interior of the bilayer membrane of the trigeminal nerve endings. There is probably only a slight, non-specific interaction between the VOC molecule and the receptor molecule, whereas this type of non-specific interaction for the detection of odor is much stronger. In practical terms, the suggestion that all VOCs share a common irritation receptor site implies that nasal-pungency thresholds of individual VOCs may be additive. Quantitative structure-activity relationships (QSARs) for nasal-pungency thresholds were also developed from the model, which can be used to predict nasal-pungency thresholds of common VOCs. Although the present model does not offer additional precision over that of M.H. Abraham et al., 1996, Fundam. Appl. Toxicol. 31, 71-76, it requires fewer descriptors and offers a physiological basis to the QSAR. Another advantage of the present model is that it also provides a basis for comparison between the olfactory process and nasal pungency.

  13. Investigation and prediction of protein precipitation by polyethylene glycol using quantitative structure-activity relationship models.

    PubMed

    Hämmerling, Frank; Ladd Effio, Christopher; Andris, Sebastian; Kittelmann, Jörg; Hubbuch, Jürgen

    2017-01-10

    Precipitation of proteins is considered to be an effective purification method for proteins and has proven its potential to replace costly chromatography processes. Besides salts and polyelectrolytes, polymers, such as polyethylene glycol (PEG), are commonly used for precipitation applications under mild conditions. Process development, however, for protein precipitation steps still is based mainly on heuristic approaches and high-throughput experimentation due to a lack of understanding of the underlying mechanisms. In this work we apply quantitative structure-activity relationships (QSARs) to model two parameters, the discontinuity point m* and the β-value, that describe the complete precipitation curve of a protein under defined conditions. The generated QSAR models are sensitive to the protein type, pH, and ionic strength. It was found that the discontinuity point m* is mainly dependent on protein molecular structure properties and electrostatic surface properties, whereas the β-value is influenced by the variance in electrostatics and hydrophobicity on the protein surface. The models for m* and the β-value exhibit a good correlation between observed and predicted data with a coefficient of determination of R(2)≥0.90 and, hence, are able to accurately predict precipitation curves for proteins. The predictive capabilities were demonstrated for a set of combinations of protein type, pH, and ionic strength not included in the generation of the models and good agreement between predicted and experimental data was achieved.

  14. Using quantitative structure activity relationship models to predict an appropriate solvent system from a common solvent system family for countercurrent chromatography separation.

    PubMed

    Marsden-Jones, Siân; Colclough, Nicola; Garrard, Ian; Sumner, Neil; Ignatova, Svetlana

    2015-06-12

    Countercurrent chromatography (CCC) is a form of liquid-liquid chromatography. It works by running one immiscible solvent (mobile phase) over another solvent (stationary phase) being held in a CCC column using centrifugal force. The concentration of compound in each phase is characterised by the partition coefficient (Kd), which is the concentration in the stationary phase divided by the concentration in the mobile phase. When Kd is between approximately 0.2 and 2, it is most likely that optimal separation will be achieved. Having the Kd in this range allows the compound enough time in the column to be separated without resulting in a broad peak and long run time. In this paper we report the development of quantitative structure activity relationship (QSAR) models to predict logKd. The QSAR models use only the molecule's 2D structure to predict the molecular property logKd.

  15. 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...

  16. Insights on Cytochrome P450 Enzymes and Inhibitors Obtained Through QSAR Studies

    PubMed Central

    Sridhar, Jayalakshmi; Liu, Jiawang; Foroozesh, Maryam; Stevens, Cheryl L. Klein

    2013-01-01

    The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions. PMID:22864238

  17. Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment

    SciTech Connect

    McKim, J.M.; Bradbury, S.P.; Niemi, G.J.

    1987-04-01

    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 aqueous 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.

  18. 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

  19. Introducing Catastrophe-QSAR. Application on Modeling Molecular Mechanisms of Pyridinone Derivative-Type HIV Non-Nucleoside Reverse Transcriptase Inhibitors

    PubMed Central

    Putz, Mihai V.; Lazea, Marius; Putz, Ana-Maria; Duda-Seiman, Corina

    2011-01-01

    The classical method of quantitative structure-activity relationships (QSAR) is enriched using non-linear models, as Thom’s polynomials allow either uni- or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the “optimal molecular structural domains” and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1 inhibition. The best molecules are characterized by hydrophobic interactions with the HIV-1 p66 subunit protein, and they concur with those identified in other 3D-QSAR analyses. Moreover, the importance of aromatic ring stacking interactions for increasing the binding affinity of the inhibitor-reverse transcriptase ligand-substrate complex is highlighted. PMID:22272148

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

    SciTech Connect

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    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 binding 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

  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. Structure activity relationships of spiramycins.

    PubMed

    Omura, S; Sano, H; Sunazuka, T

    1985-07-01

    Sixty-six derivatives of spiramycin I and neospiramycin I were synthesized and evaluated by four parameters, MIC, affinity to ribosomes (ID50), therapeutic effect in mice and retention time in HPLC. Among the derivatives, 3,3'',4''-tri-O-propionyl- and 3,4''-di-O-acetyl-3''-O-butyrylspiramycin I showed the highest therapeutic effect which was superior to acetylspiramycin. Structure activity relationships of spiramycins are discussed.

  3. Stepwise development of structure-activity relationship of diverse PARP-1 inhibitors through comparative and validated in silico modeling techniques and molecular dynamics simulation.

    PubMed

    Halder, Amit K; Saha, Achintya; Saha, Krishna Das; Jha, Tarun

    2015-01-01

    Inhibitors of poly (ADP-ribose) polymerase-1 (PARP-1) enzyme are useful for the treatment of various diseases including cancer. Comparative in silico studies were performed on different ligand-based (2D-QSAR, Kernel-based partial least square (KPLS) analysis, Pharmacophore Search Engine (PHASE) pharmacophore mapping), and structure-based (molecular docking, MM-GBSA analyses, Gaussian-based 3D-QSAR analyses on docked poses) modeling techniques to explore the structure-activity relationship of a diverse set of PARP-1 inhibitors. Two-dimensional (2D)-QSAR highlighted the importance of charge topological index (JGI7), fractional polar surface area (JursFPSA3), and connectivity index (CIC2) along with different molecular fragments. Favorable and unfavorable fingerprints were demonstrated in KPLS analysis, whereas important pharmacophore features (one acceptor, one donor, and two ring aromatic) along with favorable and unfavorable field effects were demonstrated in PHASE-based pharmacophore model. MM-GBSA analyses revealed significance of different polar, non-polar, and solvation energies. Docking-based alignment of ligands was used to perform Gaussian-based 3D-QSAR study that further demonstrated importance of different field effects. Overall, it was found that polar interactions (hydrogen bonding, bridged hydrogen bonding, and pi-cation) play major roles for higher activity. Steric groups increase the total contact surface area but it should have higher fractional polar surface area to adjust solvation energy. Structure-based pharmacophore mapping spotted the positive ionizable feature of ligands as the most important feature for discriminating highly active compounds from inactives. Molecular dynamics simulation, conducted on highly active ligands, described the dynamic behaviors of the protein complexes and supported the interpretations obtained from other modeling analyses. The current study may be useful for designing PARP-1 inhibitors.

  4. 3-Heterocycle-phenyl N-alkylcarbamates as FAAH inhibitors: design, synthesis and 3D-QSAR studies.

    PubMed

    Käsnänen, Heikki; Myllymäki, Mikko J; Minkkilä, Anna; Kataja, Antti O; Saario, Susanna M; Nevalainen, Tapio; Koskinen, Ari M P; Poso, Antti

    2010-02-01

    Carbamates are a well-established class of fatty acid amide hydrolase (FAAH) inhibitors. Here we describe the synthesis of meta-substituted phenolic N-alkyl/aryl carbamates and their in vitro FAAH inhibitory activities. The most potent compound, 3-(oxazol-2yl)phenyl cyclohexylcarbamate (2 a), inhibited FAAH with a sub-nanomolar IC(50) value (IC(50)=0.74 nM). Additionally, we developed and validated three-dimensional quantitative structure-activity relationships (QSAR) models of FAAH inhibition combining the newly disclosed carbamates with our previously published inhibitors to give a total set of 99 compounds. Prior to 3D-QSAR modeling, the degree of correlation between FAAH inhibition and in silico reactivity was also established. Both 3D-QSAR methods used, CoMSIA and GRID/GOLPE, produced statistically significant models with coefficient of correlation for external prediction (R(2) (PRED)) values of 0.732 and 0.760, respectively. These models could be of high value in further FAAH inhibitor design.

  5. Improving quantitative structure-activity relationships through multiobjective optimization.

    PubMed

    Nicolotti, Orazio; Giangreco, Ilenia; Miscioscia, Teresa Fabiola; Carotti, Angelo

    2009-10-01

    A multiobjective optimization algorithm was proposed for the automated integration of structure- and ligand-based molecular design. Driven by a genetic algorithm, the herein proposed approach enabled the detection of a number of trade-off QSAR models accounting simultaneously for two independent objectives. The first was biased toward best regressions among docking scores and biological affinities; the second minimized the atom displacements from a properly established crystal-based binding topology. Based on the concept of dominance, 3D QSAR equivalent models profiled the Pareto frontier and were, thus, designated as nondominated solutions of the search space. K-means clustering was, then, operated to select a representative subset of the available trade-off models. These were effectively subjected to GRID/GOLPE analyses for quantitatively featuring molecular determinants of ligand binding affinity. More specifically, it was demonstrated that a) diverse binding conformations occurred on the basis of the ligand ability to profitably contact different part of protein binding site; b) enzyme selectivity was better approached and interpreted by combining diverse equivalent models; and c) trade-off models were successful and even better than docking virtual screening, in retrieving at high sensitivity active hits from a large pool of chemically similar decoys. The approach was tested on a large series, very well-known to QSAR practitioners, of 3-amidinophenylalanine inhibitors of thrombin and trypsin, two serine proteases having rather different biological actions despite a high sequence similarity.

  6. 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

  7. 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.

  8. In silico study combining docking and QSAR methods on a series of matrix metalloproteinase 13 inhibitors.

    PubMed

    Xi, Lili; Li, Shuyan; Yao, Xiaojun; Wei, Yuhui; Li, Jiazhong; Liu, Huanxiang; Wu, Xin'an

    2014-11-01

    Matrix metalloproteinase 13 (MMP-13) plays an important role in the degradation of articular cartilage and has been considered as an attractive target for the treatment of osteoarthritis; hence, the development of efficient inhibitors of MMP-13 has become a hot study field. Taking a series of carboxylic acid-based MMP-13 inhibitors as research object, this work utilized an extended QSAR method to analyze the structure-activity relationships. We focused on two important topics in QSAR: bioactive conformation and descriptors. Firstly, molecular docking was carried out to dock all molecules into the MMP-13 active site in order to obtain the bioactive conformation. Secondly, based on the docked complex, descriptors characterizing receptor-ligand interactions and the ligand structure were calculated. Thirdly, a genetic algorithm (GA) and multiple linear regression (MLR) were employed to select important descriptors related to inhibitory activities, simultaneously, to build the predictive model. The built model gave satisfactory results with highly accurate fitting and strong external predictive abilities for chemicals not used in model development. Furthermore, the selected descriptors were explored to elucidate important factors influencing the inhibition activities. This study demonstrates that the selection strategy of the docking-guided bioactive conformation is rational and useful in predicting MMP-13 inhibitor activities, and receptor-ligand complex descriptors have an advantage over directly reflecting receptor-ligand interactions.

  9. 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.

  10. 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...

  11. 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.

  12. 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.

  13. Application of 3D-QSAR techniques in anti-HIV-1 drug design--an overview.

    PubMed

    Debnath, Asim Kumar

    2005-01-01

    Despite the availability of several classes of drugs against acquired immunodeficiency syndrome (AIDS) caused by human immunodeficiency virus type 1(HIV-1), this deadly disease showing very little sign of containment, especially in Sub-Saharan Africa and South-East Asia. More than 20 million people died since the first diagnosis of AIDS more than twenty years ago and almost 40 million people are currently living with HIV/AIDS. Structure-based drug design effort was immensely successful in identifying several drugs that are currently available for the treatment of HIV-1. Many applications have been reported on the use of quantitative structure-activity relationship (QSAR) studies to understand the drug-receptor interactions and help in the design of more effective analogs. Extensive application was also reported on the application of 3D-QSAR techniques, such as, Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Analysis (CoMSIA), pharmacophore generation using Catalyst/HypoGen, free-energy binding analysis, GRID/GOLPE, HINT-based techniques, etc. in anti-HIV-1 drug discovery programs in academia and industry. We have attempted to put together a comprehensive overview on the 3D-QSAR applications in anti-HIV-1 drug design reported in the literature during the last decade.

  14. A QSAR model of benzoxazole derivatives as potential inhibitors for inosine 5`-monophosphate dehydrogenase from Cryptosporidium parvum

    PubMed Central

    Teotia, Pratibha; Prakash Dwivedi, Surya; Dwivedi, Neeraja

    2016-01-01

    Cryptosporidium parvum is the common enteric protozoan pathogen causing cryptosporidiosis in human. Available drugs to treat cryptosporidiosis are ineffective and there is yet no vaccine against C. parvum. Therefore, it is of interest to design an improved yet effective drug against C. parvum. Here, we docked benzoxazole derivatives (collected from literature) with inosine 5`- monophosphate dehydrogenase (IMPDH) from Cryptosporidium parvum using the program AutoDock 4.2. The docked protein - inhibitor complex structure was optimized using molecular dynamics simulation for 5 ps with the CHARMM-22 force field using NAMD (NAnoscale Molecular Dynamics program) incorporated in visual molecular dynamics (VMD 1.9.2) and then evaluating the stability of complex structure by calculating RMSD values. NAMD is a parallel, object-oriented molecular dynamics code designed for high-performance simulation of large biomolecular systems. A quantitative structure activity relationship (QSAR) model was built using energy-based descriptors as independent variable and pIC50 value as dependent variable of fifteen known benzoxazole derivatives with C. parvum IMPDH protein, yielding correlation coefficient r2 of 0.7948. The predictive performance of QSAR model was assessed using different cross-validation procedures. Our results suggest that a ligand-receptor binding interaction for inosine 5`-monophosphate dehydrogenase using a QSAR model is promising approach to design more potent inosine 5`-monophosphate dehydrogenase inhibitors prior to their synthesis. PMID:28149045

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

    NASA Astrophysics Data System (ADS)

    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 ( {{r}}^{ 2}_{ 6 8} = 0. 9 4 , F-statistic = 125.8, {{r}}^{ 2}_{{LOO}} { = 0} . 9 2 , {{r}}^{ 2}_{{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.

  16. Genotoxicity of quinolones: substituents contribution and transformation products QSAR evaluation using 2D and 3D models.

    PubMed

    Li, Min; Wei, Dongbin; Zhao, Huimin; Du, Yuguo

    2014-01-01

    The genotoxicity of 21 quinolones antibiotics was determined using SOS/umu assay. Some quinolones exhibited high genotoxicity, and the chemical substituent on quinolone ring significantly affected genotoxicity. To establish the relationship between genotoxicity and substituent, a 2D-QSAR model based on quantum chemical parameters was developed. Calculation suggested that both steric and electrostatic properties were correlated well with genotoxicity. Furthermore, the specific effect on three key active sites (1-, 7- and 8-positions) of quinolone ring was investigated using a 3D-QSAR (comparative molecular field analysis, CoMFA) method. From our modeling, the genotoxicity increased when substituents had: (1) big volume and/or positive charge at 1-position; (2) negative charge at 7-position; and (3) small volume and/or negative charge at 8-position. The developed QSAR models were applicable to estimate genotoxicity of quinolones antibiotics and their transformation products. It is noted that some of the transformation products exhibited higher genotoxicity comparing to their precursor (e.g., ciprofloxacin). This study provided an alternative way to understand the molecule genotoxicity of quinolones derivatives, as well as to evaluate their potential environmental risks.

  17. Compatibility of functional groups in K[sup ow]-based QSARs: Application to nitro compounds

    SciTech Connect

    Banerjee, S.; Williams, C.L. )

    1993-10-01

    Nitro compounds are particular difficult to handle in simple K[sup ow]-based QSARs, owing to differences in their lipid-phase activity coefficients. These differences can be corrected, in part, through inclusion of a term in octanol solubility. A procedure for identifying potentially incompatible groups in a given QSAR is suggested. The quality of a QSAR is best if the interactions of the functional groups involved with octanol fall within a narrow range. These interactions are easily calculated by the UNIFAC method.

  18. Acute toxicity of benzophenone-type UV filters for Photobacterium phosphoreum and Daphnia magna: QSAR analysis, interspecies relationship and integrated assessment.

    PubMed

    Liu, Hui; Sun, Ping; Liu, Hongxia; Yang, Shaogui; Wang, Liansheng; Wang, Zunyao

    2015-09-01

    The hazardous potential of benzophenone (BP)-type UV filters is becoming an issue of great concern due to the wide application of these compounds in many personal care products. In the present study, the toxicities of BPs to Photobacterium phosphoreum and Daphnia magna were determined. Next, density functional theory (DFT) and comparative molecular field analysis (CoMFA) descriptors were used to obtain more detailed insight into the structure - activity relationships and to preliminarily discuss the toxicity mechanism. Additionally, the sensitivities of the two organisms to BPs and the interspecies toxicity relationship were compared. Moreover, an approach for providing a global index of the environmental risk of BPs to aquatic organisms is proposed. The results demonstrated that the mechanism underlying the toxicity of BPs to P. phosphoreum is primarily related to their electronic properties, and the mechanism of toxicity to D. magna is hydrophobicity. Additionally, D. magna was more sensitive than P. phosphoreum to most of the BPs, with the exceptions of the polyhydric BPs. Moreover, comparisons with published data revealed a high interspecies correlation coefficient among the experimental toxicity values for D. magna and Dugesia japonica. Furthermore, hydrophobicity was also found to be the most important descriptor of integrated toxicity. This investigation will provide insight into the toxicity mechanisms and useful information for assessing the potential ecological risk of BP-type UV filters.

  19. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes

    SciTech Connect

    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. - 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.

  20. CP-MLR/PLS directed QSAR study on apical sodium-codependent bile acid transporter inhibition activity of benzothiepines.

    PubMed

    Sharma, Brij Kishore; Singh, Prithvi; Pilania, Pradeep; Sarbhai, Kirti; Prabhakar, Yenamandra S

    2011-02-01

    The apical sodium-codependent bile acid transporter (ASBT) inhibition activity of benzothiepine derivatives have been analyzed based on topological and molecular features. Analysis of the structural features in conjunction with the biological endpoints in Combinatorial Protocol in Multiple Linear Regression (CP-MLR) led to the identification of 21 descriptors for modeling the activity. The study clearly suggested that the role of Randic shape index (path/walk ratio 3) and topological charges of 2-, 5-, and 6-orders to optimize the ASBT inhibitory activity of titled compounds. The influence of atomic van der Waals volumes, masses, Sanderson electronegativities, and polarizabilities are indicated via different lags of Moran and Geary autocorrelations. Presence of tertiary aromatic amine functionality in molecular structure has also shown its relevance in rationalizing the biological actions of benzothiepines. The PLS analysis has confirmed the dominance of information content of CP-MLR identified descriptors for modeling the activity when compared to those of the leftover ones.

  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. 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.

  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. Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes.

    PubMed

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2015-07-27

    A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

  5. 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.

  6. Synthesis and structure-activity relationships of 2-substituted-6-(dimethylamino)-9-(4-methylbenzyl)-9H-purines with antirhinovirus activity.

    PubMed

    Kelley, J L; Linn, J A; Selway, J W

    1989-01-01

    A series of 2-substituted-6-(dimethylamino)-9-(4-methylbenzyl)-9H-purines where the 2-substituent was H, F, Cl, CF3, CH3, CH2CH3, NH2, NHCH3, N(CH3)2, SCH3, or SO2CH3 was synthesized and tested for antirhinovirus activity to evaluate the effect of 2-substituents on antiviral activity. Intuitive and quantitative structure-activity relationship (QSAR) analysis showed that optimum antirhinovirus serotype 1B activity was associated with 9-benzylpurines that contained a C-2 lipophilic, electron-withdrawing substituent. The most active compound, 6-(dimethylamino)-9-(4-methylbenzyl)-2-(trifluoromethyl)-9H-purine (14), had an IC50 = 0.03 microM against serotype 1B, but its activity against 18 other serotypes was not uniform; the IC50s ranged over 260-fold.

  7. Synthesis, photodynamic activity, and quantitative structure-activity relationship modelling of a series of BODIPYs.

    PubMed

    Caruso, Enrico; Gariboldi, Marzia; Sangion, Alessandro; Gramatica, Paola; Banfi, Stefano

    2017-02-01

    Here we report the synthesis of eleven new BODIPYs (14-24) characterized by the presence of an aromatic ring on the 8 (meso) position and of iodine atoms on the pyrrolic 2,6 positions. These molecules, together with twelve BODIPYs already reported by us (1-12), represent a large panel of BODIPYs showing different atoms or groups as substituent of the aromatic moiety. Two physico-chemical features ((1)O2 generation rate and lipophilicity), which can play a fundamental role in the outcome as photosensitizers, have been studied. The in vitro photo-induced cell-killing efficacy of 23 PSs was studied on the SKOV3 cell line treating the cells for 24h in the dark then irradiating for 2h with a green LED device (fluence 25.2J/cm(2)). The cell-killing efficacy was assessed with the MTT test and compared with that one of meso un-substituted compound (13). In order to understand the possible effect of the substituents, a predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, was developed. The results clearly indicate that the presence of an aromatic ring is fundamental for an excellent photodynamic response, whereas the electronic effects and the position of the substituents on the aromatic ring do not influence the photodynamic efficacy.

  8. Structure-Activity Relationship of Nerve-Highlighting Fluorophores

    PubMed Central

    Gibbs, Summer L.; Xie, Yang; Goodwill, Haley L.; Nasr, Khaled A.; Ashitate, Yoshitomo; Madigan, Victoria J.; Siclovan, Tiberiu M.; Zavodszky, Maria; Tan Hehir, Cristina A.; Frangioni, John V.

    2013-01-01

    Nerve damage is a major morbidity associated with numerous surgical interventions. Yet, nerve visualization continues to challenge even the most experienced surgeons. A nerve-specific fluorescent contrast agent, especially one with near-infrared (NIR) absorption and emission, would be of immediate benefit to patients and surgeons. Currently, there are only three classes of small molecule organic fluorophores that penetrate the blood nerve barrier and bind to nerve tissue when administered systemically. Of these three classes, the distyrylbenzenes (DSBs) are particularly attractive for further study. Although not presently in the NIR range, DSB fluorophores highlight all nerve tissue in mice, rats, and pigs after intravenous administration. The purpose of the current study was to define the pharmacophore responsible for nerve-specific uptake and retention, which would enable future molecules to be optimized for NIR optical properties. Structural analogs of the DSB class of small molecules were synthesized using combinatorial solid phase synthesis and commercially available building blocks, which yielded more than 200 unique DSB fluorophores. The nerve-specific properties of all DSB analogs were quantified using an ex vivo nerve-specific fluorescence assay on pig and human sciatic nerve. Results were used to perform quantitative structure-activity relationship (QSAR) modeling and to define the nerve-specific pharmacophore. All DSB analogs with positive ex vivo fluorescence were tested for in vivo nerve specificity in mice to assess the effect of biodistribution and clearance on nerve fluorescence signal. Two new DSB fluorophores with the highest nerve to muscle ratio were tested in pigs to confirm scalability. PMID:24039960

  9. The use of Hasse diagrams as a potential approach for inverse QSAR.

    PubMed

    Brüggemann, R; Pudenz, S; Carlsen, L; Sørensen, P B; Thomsen, M; Mishra, R K

    2001-02-01

    Quantitative structure-activity relationships are often based on standard multidimensional statistical analyses and sophisticated local and global molecular descriptors. Here, the aim is to develop a tool helpful to define a molecule or a class of molecules which fulfills pre-described properties, i.e., an Inverse QSAR approach. If highly sophisticated descriptors are used in QSAR, the structure and then the synthesis recipe may be hard to derive. Thus, descriptors, from which the synthesis recipe can be easily derived, seem appropriate to be included within this study. However, if descriptors simple enough to be useful for defining syntheses recipes of chemicals were used, the accuracy of a numeric expression may fail. This paper suggests a method, based on very simple elements of the theory of partially ordered sets, to find a qualitative basis for the relationship between such fairly simple descriptors on the one side and a series of ecotoxicological properties, on the other side. The partial order ranking method assumes neither linearity nor certain statistical distribution properties. Therefore the method may be more general compared to many standard statistical techniques. A series of chlorinated aliphatic compounds has been used as an illustrative example and a comparison with more sophisticated descriptors derived from quantum chemistry and graph theory is given. Among the results, it was disclosed that only for algae lethal concentration, as one of the four ecotoxicological properties, the synthesis specific predictors seem to be good estimators. For all other ecotoxicological properties quantum chemical descriptors appear as the more suitable estimators.

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. Aquatic toxicity structure-activity relationships for the zwitterionic surfactant alkyl dimethyl amine oxide to several aquatic species and a resulting species sensitivity distribution.

    PubMed

    Belanger, Scott E; Brill, Jessica L; Rawlings, Jane M; McDonough, Kathleen M; Zoller, Ann C; Wehmeyer, Kenneth R

    2016-12-01

    Amine oxide (AO) is a cationically charged surfactant at environmental pH and has previously been assessed in the OECD (Organization for Economic Cooperation and Development) High Production Volume (HPV) chemicals program. Typical of cationic chemicals, AO is highly aquatically toxic. In this study we vastly improve the knowledge of AO toxicity by developing acute Quantitative Structure Activity Relationships (QSARs) for an alga (Desmodesmus subspicatus), an invertebrate (Daphnia magna) and a fish (Danio rerio) using the appropriate array of OECD Test Guidelines. A chronic toxicity QSAR was also determined for the most sensitive taxon, Desmodesmus. Pure AO spanning the chain lengths of C8 to C16 were tested individually with trace analytical confirmation of exposures in all tests. The QSARs were all of high quality (R(2) 0.92-0.98) with slopes ranging from -0.338 to -0.484. QSARs were then used to normalize toxicity outcomes for a larger, previously published data set used in HPV, European REACH (Registration, Evaluation, and Authorization of Chemicals), and peer reviewed publications. Two additional species, Lemna gibba (macrophyte) and Ankistrodesmus falcatus (alga) were studied in exposures to dodecyl (C12) AO to provide sufficient taxonomic diversity to conduct a Species Sensitivity Distribution (SSD) analysis. The SSD 5th percentile hazardous concentration (HC5) to C12 AO was found to be 0.052mg/L which is similar to an existing AO 28-d, 3-community periphyton community bioassay normalized to C12 AO (No-observed-effect-concentration or NOEC=0.152mg/L). The statistical properties of the SSD was probed suggesting that new studies of additional taxa would be required that were at least 10-fold more sensitive than the most sensitive taxon to move the HC5 lower by a factor of 3. The overall AO hazard assessment suggests a large margin of safety relative to published environmental exposure data.

  16. Structure-guided unravelling: Phenolic hydroxyls contribute to reduction of acrylamide using multiplex quantitative structure-activity relationship modelling.

    PubMed

    Zhang, Yu; Huang, Mengmeng; Wang, Qiao; Cheng, Jun

    2016-05-15

    We reported a structure-activity relationship study on unravelling phenolic hydroxyls instead of alcoholic hydroxyls contribute to the reduction of acrylamide formation by flavonoids. The dose-dependent study shows a close correlation between the number of phenolic hydroxyls of flavonoids and their reduction effects. In view of positions of hydroxyls, the 3',4'(ortho)-dihydroxyls in B cycle, 3-hydroxyl or hydroxyls of 3-gallate in C cycle, and 5,7(meta)-dihydroxyls in A cycle of flavonoid structures play an important role in the reduction of acrylamide. Flavone C-glycosides are more effective at reducing the formation of acrylamide than flavone O-glycosides when sharing the same aglycone. The current multiplex quantitative structure-activity relationship (QSAR) equations effectively predict the inhibitory rates of acrylamide using selected chemometric parameters (R(2): 0.835-0.938). This pioneer study opens a broad understanding on the chemoprevention of acrylamide contaminants on a structural basis.

  17. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

    PubMed Central

    Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

    2015-01-01

    Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

  18. 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

    2016-12-12

    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. 2016;9999: 1-10. © 2016 Wiley Periodicals, Inc.

  19. QSARs in Netherlands water quality management policies.

    PubMed

    van der Gaag, M A

    1991-12-01

    QSARs are a useful tool for predicting the potential toxic effects of compounds for which no data are available. Within strictly defined limits, QSARs can be applied to assess the potential impact of a spill, to evaluate ecotoxicological effects and environmental fate of organics in waste water and to set priorities for water quality criteria. For a wider application, there is a need for 'worst case' SARs providing a 'safer' estimate of toxicity than QSARs with an optimum fit, which might underestimate toxicity.

  20. Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors.

    PubMed

    Teli, Mahesh Kumar; Rajanikant, G K

    2012-08-01

    Tumour progression locus-2 (Tpl2) is a serine/threonine kinase, which regulates the expression of tumour necrosis factor α. The article describes the development of a robust pharmacophore model and the investigation of structure-activity relationship analysis of quinoline-3-carbonitrile derivatives reported for Tpl2 kinase inhibition. A five point pharmacophore model (ADRRR) was developed and used to derive a predictive atom-based 3-dimensional quantitative structure activity relationship (3D-QSAR) model. The obtained 3D-QSAR model has an excellent correlation coefficient value (r(2)= 0.96), Fisher ratio (F = 131.9) and exhibited good predictive power (q(2) = 0.79). The QSAR model suggests that the inclusion of hydrophobic substituents will enhance the Tpl2 kinase inhibition. In addition, H-bond donating groups, negative ionic groups and electron withdrawing groups positively contribute to the Tpl2 kinase inhibition. Further, pharmacophoric model was validated by the receiver operating characteristic curve analysis and was employed for virtual screening to identify six potential Tpl2 kinase inhibitors. The findings of this study provide a set of guidelines for designing compounds with better Tpl2 kinase inhibitory potency.

  1. Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

    PubMed

    Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

    2016-03-14

    The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.

  2. Quantitative structure-activity relationship of hydroxyl-substituent Schiff bases in radical-induced hemolysis of human erythrocytes.

    PubMed

    Tang, You-Zhi; Liu, Zai-Qun

    2008-01-01

    The major objective of this work was to explore the quantitative structure-activity relationship (QSAR) of hydroxyl-substituent Schiff bases in protecting human erythrocytes against 2,2'-azobis(2-amidinopropane hydrochloride) (AAPH)- induced hemolysis, in which 10 Schiff bases including 4-phenyliminomethylphenol (PIH); 4-((4-hydroxybenzylidene) amino)phenol (PAH); 2-methoxy-4-((4-hydroxyphenylimino)methyl)phenol (PMH); 4-((furan-2-ylmethylene)amino) phenol (FAH); 4-((4-N,N-dimethylaminobenzylidene)amino)phenol (PDH); 2-((4-N,N-dimethylaminobenzylidene)amino) phenol (ODH); 2-(naphthalene-1-yliminomethyl)phenol (NAH); 2-(benzyliminomethyl)phenol (BPH); 1,4-di((2-hydroxyphenylimino) methyl)benzene (DOH); 1,4-di((4-hydroxyphenylimino)methyl)benzene DPH, were available for this in vitro experimental system. The results revealed that the radical-scavenging activity of the --OH attached to the para position of methylene in Schiff base was much lower than that attached to the ortho position of the N atom. The large conjugate system and low steric hindrance in the framework of Schiff base benefit the Schiff base to trap radicals. Meanwhile, since a Schiff base, even without any substituent, can also play an antioxidative role in this experimental system, the QSAR results suggest that hydroxyl-substituent Schiff bases are potential drugs in the treatment of radical-related diseases, and provide more information for designing novel drugs.

  3. Using three-dimensional quantitative structure-activity relationships to examine estrogen receptor binding affinities of polychlorinated hydroxybiphenyls

    SciTech Connect

    Waller, C.L.; Minor, D.L.; McKinney, J.D.

    1995-07-01

    Certain phenyl-substituted hydrocarbons of environmental concern have the potential to disrupt the endocrine system of animals, apparently in association with their estrogenic properties. Competition with natural estrogens for the estrogen receptor is a possible mechanism by which such effects could occur. We used comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (QSAR) paradigm, to examine the underlying structural properties of ortho-chlorinated hydroxybiphenyl analogs known to bind to the estrogen receptor. The cross-validated and conventional statistical results indicate a high degree of internal predictability for the molecules included in the training data set. In addition to the phenolic (A) ring system, conformational restriction of the overall structure appears to play an important role in estrogen receptor binding affinity. Hydrophobic character as assessed using hydropathic interaction fields also contributes in a positive way to binding affinity. The CoMFA-derived QSARs may be useful in examining the estrogenic activity of a wider range of phenyl-substituted hydrocarbons of environmental concern. 37 refs., 2 figs., 2 tabs.

  4. 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.

  5. 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.

  6. 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.

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

    PubMed

    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.

  8. QSAR of anticancer compounds. Bis(11-oxo-11H-indeno[1,2-b]quinoline-6-carboxamides), bis(phenazine-1-carboxamides), and bis(naphthalimides).

    PubMed

    Mekapati, S B; Denny, W A; Kurup, A; Hansch, C

    2001-11-01

    QSAR have been developed for the anticancer activity (growth inhibition) of various tumor cells by bis(11-oxo-11H-indeno[1,2-b]quinoline-6-carboxamides), bis(phenazine-1-carboxamides), and bis(naphthalimides). Of the seven QSAR, positive hydrophobic interactions are found in only two examples: bis(naphthalimides) versus human colon cancer cells. This is consistent with other QSAR of anticancer compounds where hydrophobic interactions are found to be unimportant.

  9. Ranking of hair dye substances according to predicted sensitization potency: quantitative structure-activity relationships.

    PubMed

    Søsted, H; Basketter, D A; Estrada, E; Johansen, J D; Patlewicz, G Y

    2004-01-01

    Allergic contact dermatitis following the use of hair dyes is well known. Many chemicals are used in hair dyes and it is unlikely that all cases of hair dye allergy can be diagnosed by means of patch testing with p-phenylenediamine (PPD). The objectives of this study are to identify all hair dye substances registered in Europe and to provide their tonnage data. The sensitization potential of each substance was then estimated by using a quantitative structure-activity relationship (QSAR) model and the substances were ranked according to their predicted potency. A cluster analysis was performed in order to help select a number of chemically diverse hair dye substances that could be used in subsequent clinical work. Various information sources, including the Inventory of Cosmetics Ingredients, new regulations on cosmetics, data on total use and ChemId (the Chemical Search Input website provided by the National Library of Medicine), were used in order to identify the names and structures of the hair dyes. A QSAR model, developed with the help of experimental local lymph node assay data and topological sub-structural molecular descriptors (TOPS-MODE), was used in order to predict the likely sensitization potential. Predictions for sensitization potential were made for the 229 substances that could be identified by means of a chemical structure, the majority of these hair dyes (75%) being predicted to be strong/moderate sensitizers. Only 22% were predicted to be weak sensitizers and 3% were predicted to be extremely weak or non-sensitizing. Eight of the most widely used hair dye substances were predicted to be strong/moderate sensitizers, including PPD - which is the most commonly used hair dye allergy marker in patch testing. A cluster analysis by using TOPS-MODE descriptors as inputs helped us group the hair dye substances according to their chemical similarity. This would facilitate the selection of potential substances for clinical patch testing. A patch-test series

  10. Insight into the interactions between novel isoquinolin-1,3-dione derivatives and cyclin-dependent kinase 4 combining QSAR and molecular docking.

    PubMed

    Zheng, Junxia; Kong, Hao; Wilson, James M; Guo, Jialiang; Chang, Yiqun; Yang, Mengjia; Xiao, Gaokeng; Sun, Pinghua

    2014-01-01

    Several small-molecule CDK inhibitors have been identified, but none have been approved for clinical use in the past few years. A new series of 4-[(3-hydroxybenzylamino)-methylene]-4H-isoquinoline-1,3-diones were reported as highly potent and selective CDK4 inhibitors. In order to find more potent CDK4 inhibitors, the interactions between these novel isoquinoline-1,3-diones and cyclin-dependent kinase 4 was explored via in silico methodologies such as 3D-QSAR and docking on eighty-one compounds displaying potent selective activities against cyclin-dependent kinase 4. Internal and external cross-validation techniques were investigated as well as region focusing, bootstraping and leave-group-out. A training set of 66 compounds gave the satisfactory CoMFA model (q2 = 0.695, r2 = 0.947) and CoMSIA model (q2 = 0.641, r2 = 0.933). The remaining 15 compounds as a test set also gave good external predictive abilities with r2pred values of 0.875 and 0.769 for CoMFA and CoMSIA, respectively. The 3D-QSAR models generated here predicted that all five parameters are important for activity toward CDK4. Surflex-dock results, coincident with CoMFA/CoMSIA contour maps, gave the path for binding mode exploration between the inhibitors and CDK4 protein. Based on the QSAR and docking models, twenty new potent molecules have been designed and predicted better than the most active compound 12 in the literatures. The QSAR, docking and interactions analysis expand the structure-activity relationships of constrained isoquinoline-1,3-diones and contribute towards the development of more active CDK4 subtype-selective inhibitors.

  11. BioPPSy: An Open-Source Platform for QSAR/QSPR Analysis

    PubMed Central

    Walker, Michael L.

    2016-01-01

    The reliability of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. We present here BioPPSy, which aims to fill this gap by providing an easy-to-use open-source software platform. We demonstrate the program capabilities by calculating three key properties used in drug discovery, aqueous solubility, Caco-2 cell permeability and blood-brain barrier permeability. A comparison is made with a number of previously reported methods, taken from the literature, for each property. The software, including source code, current models and databases, is available from https://sourceforge.net/projects/bioppsy/. PMID:27832156

  12. 3D-QSAR analysis of a series of S-DABO derivatives as anti-HIV agents by CoMFA and CoMSIA.

    PubMed

    Xu, H R; Fu, L; Zhan, P; Liu, X Y

    2016-12-01

    In this study, we retrieved a series of 59 dihydroalkylthio-benzyloxopyrimidine (S-DABO) derivatives, which is a class of highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) reported from published articles, and analysed them with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Statistically significant three-dimensional quantitative structure-activity relationship (3D-QSAR) models by CoMFA and CoMSIA were derived from a training set of 46 compounds on the basis of the rigid body alignment. Further, the predictive ability of the QSAR models was validated by a test set of 13 compounds. Based on the information derived from CoMFA and CoMSIA contour maps, we have identified some steric and electrostatic features for improving the activities of these inhibitors, and we validated the 3D-QSAR results by a molecular docking method. On the basis of the obtained results, we designed a new series of S-DABO derivatives with high activities. Therefore, this study could be utilized to design more potent S-DABO analogues as anti-HIV agents.

  13. QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method.

    PubMed

    Kumar, Ashwani; Chauhan, Shilpi

    2017-03-01

    Silent information regulator 2 homologue one (SIRT1) modulators have therapeutic potential for a number of diseases like cardiovascular, metabolic, inflammatory and age related disorders. Here, we have studied both activators and inhibitors of SIRT1 and constructed differential quantitative structure activity relationship (QSAR) models using CORAL software by Monte Carlo optimization method and SMILES notation. 3 splits divided into 3 subsets: sub-training, calibration and test sets, were examined and validated with a prediction set. All the described models were statistically significant models. The values of sensitivity, specificity, accuracy and Matthews' correlation coefficient for the validation set of best model were 1.0000, 0.8889, 0.9524 and 0.9058, respectively. In mechanistic interpretation, structural features important for SIRT1 activation and inhibition have been defined.

  14. Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

    PubMed Central

    Yilmaz, Hayriye; Rasulev, Bakhtiyor; Leszczynski, Jerzy

    2015-01-01

    The knowledge of physico-chemical properties of carbon nanotubes, including behavior in organic solvents is very important for design, manufacturing and utilizing of their counterparts with improved properties. In the present study a quantitative structure-activity/property relationship (QSAR/QSPR) approach was applied to predict the dispersibility of single walled carbon nanotubes (SWNTs) in various organic solvents. A number of additive descriptors and quantum-chemical descriptors were calculated and utilized to build QSAR models. The best predictability is shown by a 4-variable model. The model showed statistically good results (R2training = 0.797, Q2 = 0.665, R2test = 0.807), with high internal and external correlation coefficients. Presence of the X0Av descriptor and its negative term suggest that small size solvents have better SWCNTs solubility. Mass weighted descriptor ATS6m also indicates that heavier solvents (and small in size) most probably are better solvents for SWCNTs. The presence of the Dipole Z descriptor indicates that higher polarizability of the solvent molecule increases the solubility. The developed model and contributed descriptors can help to understand the mechanism of the dispersion process and predictorganic solvents that improve the dispersibility of SWNTs.

  15. Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling.

    PubMed

    Yost, Erin E; Stanek, John; DeWoskin, Robert S; Burgoon, Lyle D

    2016-07-19

    The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids.

  16. The Use of Pseudo-equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding

    PubMed Central

    Zhu, Xiangwei; Sedykh, Alexander; Zhu, Hao; Liu, Shushen; Tropsha, Alexander

    2015-01-01

    Purpose To develop accurate in silico predictors of Plasma Protein Binding (PPB). Methods Experimental PPB data were compiled for over 1,200 compounds. Two endpoints have been considered: (1) fraction bound (%PPB); and (2) the logarithm of a pseudo binding constant (lnKa) derived from %PPB. The latter metric was employed because it reflects the PPB thermodynamics and the distribution of the transformed data is closer to normal. Quantitative Structure-Activity Relationship (QSAR) models were built with Dragon descriptors and three statistical methods. Results Five-fold external validation procedure resulted in models with the prediction accuracy (R2) of 0.67±0.04 and 0.66±0.04, respectively, and the mean absolute error (MAE) of 15.3±0.2% and 13.6±0.2%, respectively. Models were validated with two external datasets: 173 compounds from DrugBank, and 236 chemicals from the US EPA ToxCast project. Models built with lnKa were significantly more accurate (MAE of 6.2–10.7%) than those built with %PPB (MAE of 11.9–17.6%) for highly bound compounds both for the training and the external sets. Conclusion The pseudo binding constant (lnKa) is more appropriate for characterizing PPB binding than conventional %PPB. Validated QSAR models developed herein can be applied as reliable tools in early drug development and in chemical risk assessment. PMID:23568522

  17. Monte carlo method-based QSAR modeling of penicillins binding to human serum proteins.

    PubMed

    Veselinović, Jovana B; Toropov, Andrey A; Toropova, Alla P; Nikolić, Goran M; Veselinović, Aleksandar M

    2015-01-01

    The binding of penicillins to human serum proteins was modeled with optimal descriptors based on the Simplified Molecular Input-Line Entry System (SMILES). The concentrations of protein-bound drug for 87 penicillins expressed as percentage of the total plasma concentration were used as experimental data. The Monte Carlo method was used as a computational tool to build up the quantitative structure-activity relationship (QSAR) model for penicillins binding to plasma proteins. One random data split into training, test and validation set was examined. The calculated QSAR model had the following statistical parameters: r(2)  = 0.8760, q(2)  = 0.8665, s = 8.94 for the training set and r(2)  = 0.9812, q(2)  = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r(2)  = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r(2)  = 0.921 and s = 7.18. SMILES-based molecular fragments (structural indicators) responsible for the increase and decrease of penicillins binding to plasma proteins were identified. The possibility of using these results for the computer-aided design of new penicillins with desired binding properties is presented.

  18. Quantification of contributions of molecular fragments for eye irritation of organic chemicals using QSAR study.

    PubMed

    Kar, Supratik; Roy, Kunal

    2014-05-01

    The eye irritation potential of chemicals has largely been evaluated using the Draize rabbit-eye test for a very long time. The Draize eye-irritation data on 38 compounds established by the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) has been used in the present quantitative structure-activity relationship (QSAR) analysis in order to predict molar-adjusted eye scores (MES) and determine possible structural requisites and attributes that are primarily responsible for the eye irritation caused by the studied solutes. The developed model was rigorously validated internally as well as externally by applying principles of the Organization for Economic Cooperation and Development (OECD). The test for applicability domain was also carried out in order to check the reliability of the predictions. Important fragments contributing to higher MES values of the solutes were identified through critical analysis and interpretation of the developed model. Considering all the identified structural attributes, one can choose or design safe solutes with low eye irritant properties. The presented approach suggests a model for use in the context of virtual screening of relevant solute libraries. The developed QSAR model can be used to predict existing as well as future chemicals falling within the applicability domain of the model in order to reduce the use of animals.

  19. 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

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

    SciTech Connect

    Valerio, Luis G. . E-mail: luis.valerio@FDA.HHS.gov; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph 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 is 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 chemicals

  1. Megavariate analysis of hierarchical QSAR data

    NASA Astrophysics Data System (ADS)

    Eriksson, Lennart; Johansson, Erik; Lindgren, Fredrik; Sjöström, Michael; Wold, Svante

    2002-10-01

    Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate. A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called `super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated. In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.

  2. Formulation development of transdermal dosage forms: quantitative structure-activity relationship model for predicting activities of terpenes that enhance drug penetration through human skin.

    PubMed

    Kang, L; Yap, C W; Lim, P F C; Chen, Y Z; Ho, P C; Chan, Y W; Wong, G P; Chan, S Y

    2007-07-31

    Terpenes and terpenoids have been used as enhancers in transdermal formulations for facilitating penetration of drugs into human skin. Knowledge of the correlation between the human skin penetration effect (HSPE) and the physicochemical properties of these enhancers is important for facilitating the discovery and development of more enhancers. In this work, the HSPE of 49 terpenes and terpenoids were compared by the in vitro permeability coefficients of haloperidol (HP) through excised human skin. A first-order multiple linear regression (MLR) model was constructed to link the permeability coefficient of the drug to the lipophilicity, molecular weight, boiling point, the terpene type and the functional group of each enhancer. The Quantitative Structure-Activity Relationship (QSAR) model was derived from our data generated by using standardized experimental protocols, which include: HP in propylene glycol (PG) of 3 mg/ml as the donor solution containing 5% (w/v) of the respective terpene, the same composition and volume of receptor solution, similar human skin samples, in the same set of automated flow-through diffusion cells. The model provided a simple method to predict the enhancing effects of terpenes for drugs with physicochemical properties similar to HP. Our study suggested that an ideal terpene enhancer should possess at least one or combinations of the following properties: hydrophobic, in liquid form at room temperature, with an ester or aldehyde but not acid functional group, and is neither a triterpene nor tetraterpene. Possible mechanisms revealed by the QSAR model were discussed.

  3. 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.

  4. A new strategy to improve the predictive ability of the local lazy regression and its application to the QSAR study of melanin-concentrating hormone receptor 1 antagonists.

    PubMed

    Li, Jiazhong; Li, Shuyan; Lei, Beilei; Liu, Huanxiang; Yao, Xiaojun; Liu, Mancang; Gramatica, Paola

    2010-04-15

    In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model.

  5. A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

    PubMed

    Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong

    2010-07-30

    Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

  6. 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.

  7. Acaricidal and quantitative structure activity relationship of monoterpenes against the two-spotted spider mite, Tetranychus urticae.

    PubMed

    Badawy, Mohamed E I; El-Arami, Sailan A A; Abdelgaleil, Samir A M

    2010-11-01

    The acaricidal activity of 12 monoterpenes against the two-spotted spider mite, Tetranychus urticae Koch, was examined using fumigation and direct contact application methods. Cuminaldehyde and (-)-linalool showed the highest fumigant toxicity with LC(50) = 0.31 and 0.56 mg/l, respectively. The other monoterpenes exhibited a strong fumigant toxicity, the LC(50) values ranging from 1.28 to 8.09 mg/l, except camphene, which was the least effective (LC(50) = 61.45 mg/l). Based on contact activity, the results were rather different: menthol displayed the highest acaricidal activity (LC(50) = 128.53 mg/l) followed by thymol (172.0 mg/l), geraniol (219.69 mg/l) and (-)-limonene (255.44 mg/l); 1-8-cineole, cuminaldehyde and (-)-linalool showed moderate toxicity. At 125 mg/l, (-)-Limonene and (-)-carvone caused the highest egg mortality among the tested compounds (70.6 and 66.9% mortality, respectively). In addition, the effect of molecular descriptors was also analyzed using the quantitative structure activity relationship (QSAR) procedure. The QSAR model showed excellent agreement between the estimated and experimentally measured toxicity parameter (LC(50)) for the tested monoterpenes and the fumigant activity increased significantly with the vapor pressure. Comparing the results of the fumigant and contact toxicity assays of monoterpenes against T. urticae with the results of acetylcholinesterase (AChE) inhibitory effect revealed that some of the tested compounds showed a strong acaricidal activity and a potent AChE inhibitory activity, such as cuminaldehyde, (-)-linalool, (-)-limonene and menthol. However, other compounds such as (-)-carvone revealed a strong fumigant activity but a weak AChE inhibitory activity.

  8. Prediction of PAH mutagenicity in human cells by QSAR classification.

    PubMed

    Papa, E; Pilutti, P; Gramatica, P

    2008-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

  9. Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques

    NASA Astrophysics Data System (ADS)

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

    2006-02-01

    A linear quantitative-structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity. For the selection of the best variables the Elimination Selection-Stepwise Regression Method (ES-SWR) is utilized. The predictive ability of the model is evaluated against a set of 13 compounds. Based on the produced QSAR model and an analysis on the domain of its applicability, the effects of various structural modifications on biological activity are investigated. The study leads to a number of guanidine derivatives with significantly improved predicted activities.

  10. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.

    PubMed

    Riera-Fernández, Pablo; Martín-Romalde, Raquel; Prado-Prado, Francisco J; Escobar, Manuel; Munteanu, Cristian R; Concu, Riccardo; Duardo-Sanchez, Aliuska; González-Díaz, Humberto

    2012-01-01

    Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and

  11. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence.

    PubMed

    Dea-Ayuela, María Auxiliadora; Pérez-Castillo, Yunierkis; Meneses-Marcel, Alfredo; Ubeira, Florencio M; Bolas-Fernández, Francisco; Chou, Kuo-Chen; González-Díaz, Humberto

    2008-08-15

    The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without

  12. Three-dimensional quantitative structure-activity relationship study on anti-cancer activity of 3,4-dihydroquinazoline derivatives against human lung cancer A549 cells

    NASA Astrophysics Data System (ADS)

    Cho, Sehyeon; Choi, Min Ji; Kim, Minju; Lee, Sunhoe; Lee, Jinsung; Lee, Seok Joon; Cho, Haelim; Lee, Kyung-Tae; Lee, Jae Yeol

    2015-03-01

    A series of 3,4-dihydroquinazoline derivatives with anti-cancer activities against human lung cancer A549 cells were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular similarity indices analysis (CoMSIA) approaches. The most potent compound, 1 was used to align the molecules. As a result, the best prediction was obtained with CoMSIA combined the steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields (q2 = 0.720, r2 = 0.897). This model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.923 as well as the scrambling stability test. This model would guide the design of potent 3,4-dihydroquinazoline derivatives as anti-cancer agent for the treatment of human lung cancer.

  13. Rigorous Incorporation of Tautomers, Ionization Species, and Different Binding Modes into Ligand-Based and Receptor-Based 3D-QSAR Methods

    PubMed Central

    Natesan, Senthil; Balaz, Stefan

    2013-01-01

    Speciation of drug candidates and receptors caused by ionization, tautomerism, and/or covalent hydration complicates ligand- and receptor-based predictions of binding affinities by 3-dimensional structure-activity relationships (3D-QSAR). The speciation problem is exacerbated by tendency of tautomers to bind in multiple conformations or orientations (modes) in the same binding site. New forms of the 3D-QSAR correlation equations, capable of capturing this complexity, can be developed using the time hierarchy of all steps that lie behind the monitored biological process – binding, enzyme inhibition or receptor activity. In most cases, reversible interconversions of individual ligand and receptor species can be treated as quickly established equilibria because they are finished in a small fraction of the exposure time that is used to determine biological effects. The speciation equilibria are satisfactorily approximated by invariant fractions of individual ligand and receptor species for buffered experimental or in vivo conditions. For such situations, the observed drug-receptor association constant of a ligand is expressed as the sum of products, for each ligand and receptor species pair, of the association microconstant and the fractions of involved species. For multiple binding modes, each microconstant is expressed as the sum of microconstants of individual modes. This master equation leads to new 3D-QSAR correlation equations integrating the results of all molecular simulations or calculations, which are run for each ligand-receptor species pair separately. The multispecies, multimode 3D-QSAR approach is illustrated by a ligand-based correlation of transthyretin binding of thyroxine analogs and by a receptor-based correlation of inhibition of MK2 by benzothiophenes and pyrrolopyrimidines. PMID:23170882

  14. Molecular modeling studies of phenoxypyrimidinyl imidazoles as p38 kinase inhibitors using QSAR and docking.

    PubMed

    Ravindra, G K; Achaiah, G; Sastry, G N

    2008-04-01

    p38 Kinase plays a vital role in inflammation mediated by tumor necrosis factor-alpha (TNFalpha) and interleukin-1beta (IL-1beta) pathways and inhibitors of p38 kinase provide effective approach for the treatment of inflammatory diseases. Pyridinyl and pyrimidinyl imidazoles, selectively inhibit p38alpha MAP kinase, are useful in the treatment of inflammatory diseases like rheumatoid arthritis. Three dimensional quantitative structure-activity relationship studies (3D-QSAR) involving comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) and molecular docking were performed on 44 phenoxypyrimidinyl imidazole p38 kinase inhibitors to find out the structural relationship with the activity. The best predictive CoMFA model with atom fit alignment resulted in cross-validated r(2) value of 0.553, noncross-validated r(2) value of 0.908 and standard error of estimate 0.187. Similarly the best predictive CoMSIA model was derived with q(2) of 0.508, noncross-validated r(2) of 0.894 and standard error of estimate of 0.197, comprising steric, electrostatic, hydrophobic and hydrogen bond donor fields. These models were able to predict the activity of test set molecules efficiently within an acceptable error range. GOLD and FlexX were employed to dock the inhibitors into the active site of the p38 kinase and these docking studies revealed the vital interactions and binding conformation of the inhibitors. The information rendered by 3D-QSAR models and the docking interactions may afford valuable clues to optimize the lead and design new potential inhibitors.

  15. Docking, 3D-QSAR studies and in silico ADME prediction on c-Src tyrosine kinase inhibitors.

    PubMed

    Tintori, Cristina; Magnani, Matteo; Schenone, Silvia; Botta, Maurizio

    2009-03-01

    Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis were performed on a wide set of c-Src inhibitors. The study was conducted using a structure-based alignment and by applying the GRID/GOLPE approach. The present 3D-QSAR investigation proved to be of good statistical value, displaying r(2), q(2) and cross-validation SDEP values of 0.94, 0.84 and 0.42, respectively. Moreover, such a model also proved to be capable of predicting the activities of an external test set of compounds. The availability of the 3D structure of the target made possible the interpretation of steric and electrostatic maps within the binding site environment and provided useful insight into the structural requirements for inhibitory activity against c-Src. Two regions whose occupation by hydrophobic portions of ligands would favourably affect the activity were clearly identified. Moreover, hydrogen bond interactions involving residues Met343, Asp406 and Ser347 emerged as playing a key role in determining the affinity of the active inhibitors toward c-Src. Furthermore, the inhibitors bearing a basic nitrogen provided enhanced potency through protonation and salt bridge formation with Asp350. A preliminary pharmacokinetic profile of the molecules under analysis was also drawn on the basis of Volsurf predictions.

  16. Quantitative structure-activity relationship study using refractotopological state atom index on some neonicotinoid insecticides.

    PubMed

    Debnath, Bikash; Gayen, Shovanlal; Basu, Anindya; Ghosh, Balaram; Srikanth, Kolluru; Jha, Tarun

    2004-12-01

    Importance of atom-level topological descriptors like electrotopological state atom (E-state) index in QSAR study is increasing. These descriptors help to relate structure and activity at atomic/fragmental level. In view of the earlier success of E-state index on some azidopyridinyl neonicotinoid insecticides, a relatively new atom-level topological descriptor; refractotopological state atom (R-state) index was used in this work. This was used to identify the important atoms/fragments related to dispersive/van der Waals interactions of neonicotinoids with the nicotinic acetylcholine receptor (nAChR). This study showed the structural requirements for the mammal alpha(4)beta(2) and Drosophila nAChR agonistic activity. It also revealed that substituted imine, nitromethylene at X-position were selective to the insecticidal activity. Azido substitution at pyridine ring of neonicotinoids disfavored the binding with the receptors. This study confirmed the validity of the R-state index as a new tool for quantitative structure-activity relationships. It has the ability to find out the required structural features as well as to predict the activity of the neonicotinoids.

  17. 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.

  18. Novel anti-tumour barringenol-like triterpenoids from the husks of Xanthoceras sorbifolia Bunge and their three dimensional quantitative structure activity relationships analysis.

    PubMed

    Wang, Da; Su, Dan; Yu, Bin; Chen, Chuming; Cheng, Li; Li, Xianzhe; Xi, Ronggang; Gao, Huiyuan; Wang, Xiaobo

    2017-01-01

    The high edible oil content of Xanthoceras sorbifolia Bunge seeds contributes to its economic value. In this study, we analysed the barrigenol-like triterpenoids derived from X. sorbifolia husks. We also identified anti-tumour agents that could enhance the health benefits and medicinal value of X. sorbifolia. We isolated 10 barrigenol triterpenoids, including six new compounds (1-6) and four known compounds (7-10). New compounds 3 and 5 showed significant inhibitory activity against the proliferation of three human tumour cell lines, namely, HepG2, HCT-116 and U87-MG. We determined the relationship between the structures and inhibitory activity of 25 barrigenol triterpenoids and 15 penta-cyclic triterpenoids through analysis of three-dimensional quantitative structure activity relationships (3D-QSAR). The isolation of novel barrigenol derivatives with anti-tumour activity from X. sorbifolia implied that husks of this plant may be a good source of anti-tumour agents.

  19. Are Mechanistic and Statistical QSAR Approaches Really Different? MLR Studies on 158 Cycloalkyl-Pyranones.

    PubMed

    Bhhatarai, Barun; Garg, Rajni; Gramatica, Paola

    2010-07-12

    Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki ) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models.

  20. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    A web accessible software tool is being developed to predict the toxicity of unknown chemicals for a wide variety of endpoints. The tool will enable a user to easily predict the toxicity of a query compound by simply entering its structure in a 2-dimensional (2-D) chemical sketc...

  1. Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure-Activity Relationship (QSAR) Models.

    PubMed

    Shao, Zheng; Hirayama, Yuya; Yamanishi, Yoshihiro; Saigo, Hiroto

    2015-12-28

    Graph data are becoming increasingly common in machine learning and data mining, and its application field pervades to bioinformatics and cheminformatics. Accordingly, as a method to extract patterns from graph data, graph mining recently has been studied and developed rapidly. Since the number of patterns in graph data is huge, a central issue is how to efficiently collect informative patterns suitable for subsequent tasks such as classification or regression. In this paper, we consider mining discriminative subgraphs from graph data with multiple labels. The resulting task has important applications in cheminformatics, such as finding common functional groups that trigger multiple drug side effects, or identifying ligand functional groups that hit multiple targets. In computational experiments, we first verify the effectiveness of the proposed approach in synthetic data, then we apply it to drug adverse effect prediction problem. In the latter dataset, we compared the proposed method with L1-norm logistic regression in combination with the PubChem/Open Babel fingerprint, in that the proposed method showed superior performance with a much smaller number of subgraph patterns. Software is available from https://github.com/axot/GLP.

  2. 3D-QSAR, molecular docking and molecular dynamics studies of a series of RORγt inhibitors.

    PubMed

    Wang, Fangfang; Yang, Wei; Shi, Yonghui; Le, Guowei

    2015-09-01

    The discovery of clinically relevant inhibitors of retinoic acid receptor-related orphan receptor-gamma-t (RORγt) for autoimmune diseases therapy has proven to be a challenging task. In the present work, to find out the structural features required for the inhibitory activity, we show for the first time a three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for a series of novel thiazole/thiophene ketone amides with inhibitory activity at the RORγt receptor. The optimum CoMFA and CoMSIA models, derived from ligand-based superimposition I, exhibit leave-one-out cross-validated correlation coefficient (R(2)cv) of .859 and .805, respectively. Furthermore, the external predictive abilities of the models were evaluated by a test set, producing the predicted correlation coefficient (R(2)pred) of .7317 and .7097, respectively. In addition, molecular docking analysis was applied to explore the binding modes between the inhibitors and the receptor. MD simulation and MM/PBSA method were also employed to study the stability and rationality of the derived conformations, and the binding free energies in detail. The QSAR models and the results of molecular docking, MD simulation, binding free energies corroborate well with each other and further provide insights regarding the development of novel RORγt inhibitors with better activity.

  3. A Combined Quantitative Structure-Activity Relationship Research of Quinolinone Derivatives as Androgen Receptor Antagonists.

    PubMed

    Wang, Yuwei; Bai, Fang; Cao, Hong; Li, Jiazhong; Liu, Huanxiang; Gramatica, Paola

    2015-01-01

    Antiandrogens bicalutamide, flutamide and enzalutamide etc. have been used in clinical trials to treat prostate cancer by binding to and antagonizing androgen receptor (AR). Although initially effective, the drug resistance problem will emerge eventually, which results in a high medical need for novel AR antagonist exploitation. Here in this work, to facilitate the rational design of novel AR antagonists, we studied the structure-activity relationships of a series of 2-quinolinone derivatives and investigated the structural requirements for their antiandrogenic activities. Different modeling methods, including 2D MLR, 3D CoMFA and CoMSIA, were implemented to evolve QSAR models. All these models, thoroughly validated, demonstrated satisfactory results especially for the good predictive abilities. The contour maps from 3D CoMFA and CoMSIA models provide visualized explanation of key structural characteristics relevant to the antiandrogenic activities, which is summarized to a position-specific conclusion at the end. The obtained results from this research are practically useful for rational design and screening of promising chemicals with high antiandrogenic activities.

  4. Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands

    PubMed Central

    2015-01-01

    The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure–activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs. PMID:24410373

  5. 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.

  6. Toxicity of substituted anilines to Pseudokirchneriella subcapitata and quantitative structure-activity relationship analysis for polar narcotics.

    PubMed

    Chen, Chung-Yuan; Ko, Chia-Wen; Lee, Po-I

    2007-06-01

    This study evaluated the toxic effects of substituted anilines on Pseudokirchneriella subcapitata with the use of a closed algal toxicity testing technique with no headspace. Two response endpoints (i.e., dissolved oxygen production [DO] and algal growth rate) were used to evaluate the toxicity of anilines. Both DO and growth rate endpoints revealed similar sensitivity to the effects of anilines. However, trichloroanilines showed stronger inhibitory effects on microalgal photosynthetic reactions than that on algal growth. For various aquatic organisms, the relative sensitivity relationship for anilines is Daphnia magna > luminescent bacteria (Microtox) > or = Pocelia reticulata > or = Pseudokirchneriella subcapitata > or = fathead minnow > Tetrahymena pyriformis. The susceptibility of P. subcapitata to anilines is similar to fish, but P. subcapitata is apparently less sensitive than the water flea. The lack of correlation between the toxicity revealed by different aquatic organisms (microalgae, D. magna, luminescent bacteria, and P. reticulata) suggests that anilines might have different metabolic routes in these organisms. Both hydrogen bonding donor capacity (the lowest unoccupied molecular orbital energy, Elumo) and hydrophobicity (1-octanol:water partition coefficient, Kow) were found to provide satisfactory descriptions for the toxicity of polar narcotics (substituted anilines and chlorophenols). Quantitative structure-activity relationships (QSARs) based on Elumo, log Kow, or both values were established with r2 values varying from 0.75 to 0.92. The predictive power for the QSAR models were found to be satisfactory through leave-one-out cross-validation. Such relationships could provide useful information for the estimation of toxicity for other polar narcotic compounds.

  7. Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches.

    PubMed

    Dashtbozorgi, Zahra; Golmohammadi, Hassan

    2010-06-01

    In this work a quantitative structure-activity relationship (QSAR) technique was developed to investigate the air to liver partition coefficient (log Kliver) for volatile organic compounds (VOCs). Suitable set of molecular descriptors was calculated and the important descriptors were selected by GA-PLS methods. These variables were served as inputs to generate neural networks. After optimization and training of the networks, they were used for the calculation of log Kliver for the validation set. The root mean square errors for the neural network calculated log Kliver of training, test, and validation sets are 0.100, 0.091, and 0.112, respectively. Results obtained reveal the reliability and good predictivity of neural network for the prediction of air to liver partition coefficient for volatile organic compounds.

  8. QSAR studies of bioconcentration factors of polychlorinated biphenyls (PCBs) using DFT, PCS and CoMFA.

    PubMed

    Liu, Hui; Liu, Hongxia; Sun, Ping; Wang, Zunyao

    2014-11-01

    The bioconcentration factors (BCFs) of 58 polychlorinated biphenyls (PCBs) were modeled by quantitative structure-activity relationship (QSAR) using density functional theory (DFT), the position of Cl substitution (PCS) and comparative molecular field analysis (CoMFA) methods. All the models were robust and predictive, and especially, the best CoMFA model was significant with a correlation coefficient (R(2)) of 0.926, a cross-validation correlation coefficient (Q(2)) of 0.821 and a root mean square error estimated (RMSE) of 0.235. The results indicate that the electrostatic descriptors play a more significant role in BCFs of PCBs. Additionally, a test set was used to compare the predictive ability of our models to others, and results show that our CoMFA model present the lowest RMSE. Thus, the models obtain in this work can be used to predict the BCFs of remaining 152 PCBs without available experimental values.

  9. QSAR and docking studies of anthraquinone derivatives by similarity cluster prediction.

    PubMed

    Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V

    2016-01-01

    Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing log P and LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor.

  10. 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

  11. 2D QSAR Study for Gemfibrozil Glucuronide as the Mechanism-based Inhibitor of CYP2C8

    PubMed Central

    Taxak, N.; Bharatam, P. V.

    2013-01-01

    Mechanism-based inhibition of cytochrome P450 involves the bioactivation of the drug to a reactive metabolite, which leads to cytochrome inhibition via various mechanisms. This is generally seen in the Phase I of drug metabolism. However, gemfibrozil (hypolipidemic drug) leads to mechanism-based inhibition after generating glucuronide conjugate (gemfibrozil acyl-β-glucuronide) in the Phase II metabolism reaction. The mechanism involves the covalent binding of the benzyl radical (generated from the oxidation of aromatic methyl group in conjugate) to the heme of CYP2C8. This article deals with the development of a 2D QSAR model based on the inhibitory potential of gemfibrozil, its analogues and corresponding glucuronide conjugates in inhibiting the CYP2C8-catalysed amodiaquine N-deethylation. The 2D QSAR model was developed using multiple linear regression analysis in Accelrys Discovery Studio 2.5 and helps in identifying the descriptors, which are actually contributing to the inhibitory potency of the molecules studied. The built model was further validated using leave one out method. The best quantitative structure activity relationship model was selected having a correlation coefficient (r) of 0.814 and cross-validated correlation coefficient (q2) of 0.799. 2D QSAR revealed the importance of volume descriptor (Mor15v), shape descriptor (SP09) and 3D matrix-based descriptor (SpMax_RG) in defining the activity for this series of molecules. It was observed that volume and 3D matrix-based descriptors were crucial in imparting higher potency to gemfibrozil glucuronide conjugate, as compared with other molecules. The results obtained from the present study may be useful in predicting the inhibitory potential (IC50 for CYP2C8 inhibition) of the glucuronide conjugates of new molecules and compare with the standard gemfibrozil acyl-β-glucuronide (in terms of pIC50 values) in early stages of drug discovery and development. PMID:24591743

  12. QSAR investigation of NaV1.7 active compounds using the SVM/Signature approach and the Bioclipse Modeling platform.

    PubMed

    Norinder, Ulf; Ek, Maria E

    2013-01-01

    A quantitative structure-activity relationship investigation of some NaV1.7 active compounds has been performed by repeated, random, external test set experiments employing structural descriptors (fingerprints) of signature type in combination with support vector machine (SVM) analysis using the radial basis function (RBF) kernel. The results from the investigation show remarkably stable performance from the derived in silico models in terms of statistical measures such as correlation coefficients as well as root mean squared errors (RMSEs) for the randomly selected external test sets. Also, the Bioclipse Modeling platform is utilized for introducing interpretation to the derived models.

  13. Molecular docking and 3D-quantitative structure activity relationship analyses of peptidyl vinyl sulfones: Plasmodium Falciparum cysteine proteases inhibitors

    NASA Astrophysics Data System (ADS)

    Teixeira, Cátia; Gomes, José R. B.; Couesnon, Thierry; Gomes, Paula

    2011-08-01

    Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (39 molecules) of peptidyl vinyl sulfone derivatives as potential Plasmodium Falciparum cysteine proteases inhibitors. Two different methods of alignment were employed: (i) a receptor-docked alignment derived from the structure-based docking algorithm GOLD and (ii) a ligand-based alignment using the structure of one of the ligands derived from a crystal structure from the PDB databank. The best predictions were obtained for the receptor-docked alignment with a CoMFA standard model ( q 2 = 0.696 and r 2 = 0.980) and with CoMSIA combined electrostatic, and hydrophobic fields ( q 2 = 0.711 and r 2 = 0.992). Both models were validated by a test set of nine compounds and gave satisfactory predictive r 2 pred values of 0.76 and 0.74, respectively. CoMFA and CoMSIA contour maps were used to identify critical regions where any change in the steric, electrostatic, and hydrophobic fields may affect the inhibitory activity, and to highlight the key structural features required for biological activity. Moreover, the results obtained from 3D-QSAR analyses were superimposed on the Plasmodium Falciparum cysteine proteases active site and the main interactions were studied. The present work provides extremely useful guidelines for future structural modifications of this class of compounds towards the development of superior antimalarials.

  14. 3D-QSAR and molecular modeling of HIV-1 integrase inhibitors

    NASA Astrophysics Data System (ADS)

    Makhija, Mahindra T.; Kulkarni, Vithal M.

    2002-03-01

    Three-dimensional quantitative structure-activity relationship (3D QSAR) methods were applied on a series of inhibitors of HIV-1 integrase with respect to their inhibition of 3'-processing and 3'-end joining steps in vitro.The training set consisted of 27 compounds belonging to the class of thiazolothiazepines. The predictive ability of each model was evaluated using test set I consisting of four thiazolothiazepines and test set II comprised of seven compounds belonging to an entirely different structural class of coumarins. Maximum Common Substructure (MCS) based method was used to align the molecules and this was compared with other known methods of alignment. Two methods of 3D QSAR: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were analyzed in terms of their predictive abilities. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of these compounds and the steric and electrostatic fields around them. CoMSIA models with considerable internal as well as external predictive ability were obtained. A poor correlation obtained with hydrophobic field indicates that the binding of thiazolothiazepines to HIV-1 integrase is mainly enthalpic in nature. Further the most active compound of the series was docked into the active site using the crystal structure of integrase. The binding site was formed by the amino acid residues 64-67, 116, 148, 151-152, 155-156, and 159. The comparison of coefficient contour maps with the steric and electrostatic properties of the receptor shows high level of compatibility.

  15. Interspecies quantitative structure-activity-activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols.

    PubMed

    Furuhama, A; Hasunuma, K; Aoki, Y

    2015-01-01

    We propose interspecies quantitative structure-activity-activity relationships (QSAARs), that is, QSARs with descriptors, to estimate species-specific acute aquatic toxicity. Using training datasets consisting of more than 100 aromatic amines and phenols, we found that the descriptors that predicted acute toxicities to fish (Oryzias latipes) and algae were daphnia toxicity, molecular weight (an indicator of molecular size and uptake) and selected indicator variables that discriminated between the absence or presence of various substructures. Molecular weight and the selected indicator variables improved the goodness-of-fit of the fish and algae toxicity prediction models. External validations of the QSAARs proved that algae toxicity could be predicted within 1.0 log unit and revealed structural profiles of outlier chemicals with respect to fish toxicity. In addition, applicability domains based on leverage values provided structural alerts for the predicted fish toxicity of chemicals with more than one hydroxyl or amino group attached to an aromatic ring, but not for fluoroanilines, which were not included in the training dataset. Although these simple QSAARs have limitations, their applicability is defined so clearly that they may be practical for screening chemicals with molecular weights of ≤364.9.

  16. Quantitative Structure-Cytotoxic Activity Relationship 1-(Benzoyloxy)urea and Its Derivative

    PubMed Central

    Hardjono, Suko; Siswodihardjo, Siswandono; Pramono, Purwanto; Darmanto, Win

    2016-01-01

    Drug development is originally carried out on a trial and error basis and it is cost-prohibitive. To minimize the trial and error risks, drug design is needed. One of the compound development processes to get a new drug is by designing a structure modification of the mother compound whose activities are recognized. A substitution of the mother compounds alters the physicochemical properties: lipophilic, electronic and steric properties. In Indonesia, one of medical treatments to cure cancer is through chemotherapy and hydroxyurea. Some derivatives, phenylthiourea, phenylurea, benzoylurea, thiourea and benzoylphenylurea, have been found to be anticancer drug candidates. To predict the activity of the drug compound before it is synthesized, the in-silico test is required. From the test, Rerank Score which is the energy of interaction between the receptor and the ligand molecule is then obtained. Hydroxyurea derivatives were synthesized by modifying Schotten-Baumann’s method by the addition of benzoyl group and its homologs resulted in the increase of lipophilic, electronic and steric properties, and cytotoxic activity. Synthesized compounds were 1-(benzoyloxy)urea and its derivatives. Structure characterization was obtained by the spectrum of UV, IR, H NMR, C NMR and Mass Spectrometer. Anticancer activity was carried out using MTT method on HeLa cells. The Quantitative Structure-Cytotoxic Activity Relationships of 1-(benzoyloxy)urea compound and its derivatives was calculated using SPSS. The chemical structure was described, namely: ClogP, π, σ, RS, CMR and Es; while, the cytotoxic activity was indicated by log (1 / IC50). The results show that the best equation of Quantitative Structure-Cytotoxic Activity Relationships (QSAR) of 1- (benzoyloxy)urea compound and its derivatives is Log 1/IC50 = - 0.205 (+ 0.068) σ – 0.051 (+ 0.022) Es – 1.911 (+ 0.020) PMID:27222144

  17. Docking and three-dimensional quantitative structure-activity relationship analyses of imidazole and thiazolidine derivatives as Aurora A kinase inhibitors.

    PubMed

    Im, Chaeuk

    2016-12-01

    Aurora A kinase is involved in the inactivation of apoptosis leading to ovarian, breast, colon, and pancreatic cancers. Inhibitors of Aurora A kinase promote aberrant mitosis resulting in arrest at a pseudo G1 state to induce mitotic catastrophe, ultimately leading to apoptosis. In this study, ligand-based and docking-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses of imidazole and thiazolidine derivatives as potential Aurora A kinase inhibitors were performed. The results provided highly reliable and predictive 3D-QSAR comparative molecular similarity index analysis (CoMSIA) models with a cross-validated q(2) value of 0.768, non-cross-validated r(2) value of 0.983, and predictive coefficient [Formula: see text] value of 0.978. CoMSIA contour maps suggested that the NH and benzyl hydroxy groups in R9, and the CO group in the thiazolidine ring and pyridine ring were important components for biological activity. The maps also suggest that the introduction of hydroxy groups at C2 of the imino-phenyl ring, C5 in the pyridine ring, or the substitution of the imino-phenyl ring for the imino-2-pyridine ring could be applied to enhance biological activity.

  18. A QSAR for the hydroxyl radical reaction rate constant: validation, domain of application, and prediction

    NASA Astrophysics Data System (ADS)

    Öberg, Tomas

    A large number of anthropogenic organic chemicals are emitted into the troposphere. Reactions with the hydroxyl radical are a dominant removal pathway for most organic compounds, but experimentally determined gas-phase reaction rate constants are only available for about 750 compounds. The lack of experimental data increases the importance of applying quantitative structure-activity relationships (QSAR) to evaluate and predict reactivities. It is generally acknowledged that these empirical relationships are valid only within the same domain for which they were developed. However, model validation is sometimes neglected and the application domain is not always well defined. The purpose of this paper is to outline how validation and domain definition can facilitate the modeling and prediction of the hydroxyl radical reaction rates for a large database. A substantial number of theoretical descriptors (867) were generated from 2D molecular structures for compounds present in the Syracuse Research Corporation's PhysProp Database. A QSAR model was developed for the hydroxyl radical reaction rate constant using a projection-based regression technique, partial least squares regression (PLSR). The PLSR model was subsequently validated with an external test set. The main factors of variation could be attributed to two reaction pathways, hydrogen atom abstraction and addition to double bonds or aromatic systems. A set of 17 293 compounds, drawn from the PhysProp Database, was projected onto the PLSR model and 74% were inside the applicability domain. The predicted hydroxyl reaction rates for 25% of these compounds were slow or negligible, with atmospheric half-lives in the range from days to years. Finally, the list of persistent organic compounds was matched against the OECD list of high production volume chemicals (HPVC). Together with the experimental data, nearly three hundred compounds were identified as both persistent and in high volume production.

  19. 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.

  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.

  1. Optimizing QSAR models for predicting ligand binding to the drug-metabolizing cytochrome P450 isoenzyme CYP2D6.

    PubMed

    Saraceno, Marilena; Massarelli, Ilaria; Imbriani, Marcello; James, Thomas L; Bianucci, Anna M

    2011-08-01

    The cytochrome P450 isozyme CYP2D6 binds a large variety of drugs, oxidizing many of them, and plays a crucial role in establishing in vivo drug levels, especially in multidrug regimens. The current study aimed to develop reliable predictive models for estimating the CYP2D6 inhibition properties of drug candidates. Quantitative structure-activity relationship (QSAR) studies utilizing 51 known CYP2D6 inhibitors were carried out. Performance achieved using models based on two-dimensional (2D) molecular descriptors was compared with performance using models entailing additional molecular descriptors that depend upon the three-dimensional (3D) structure of ligands. To properly compute the descriptors, all the 3D inhibitor structures were optimized such that induced-fit binding of the ligand to the active site was accommodated. CODESSA software was used to obtain equations for correlating the structural features of the ligands to their pharmacological effects on CYP2D6 (inhibition). The predictive power of all the QSAR models obtained was estimated by applying rigorous statistical criteria. To assess the robustness and predictability of the models, predictions were carried out on an additional set of known molecules (prediction set). The results showed that only models incorporating 3D descriptors in addition to 2D molecular descriptors possessed the requisite high predictive power for CYP2D6 inhibition.

  2. QSAR of heterocyclic antifungal agents by flip regression

    NASA Astrophysics Data System (ADS)

    Deeb, Omar; Clare, Brian W.

    2008-12-01

    QSAR analysis of a set of 96 heterocyclics with antifungal activity was performed. The results reveals that a pyridine ring is more favorable than benzene as the 6-membered ring, for high activity, but thiazole is unfavorable as the 5-membered ring relative to imidazole or oxazole. Methylene is the spacer leading to the highest activity. The descriptors used are indicator variables, which account for identity of substituent, lipophilicity and volume of substituent, and total polarizability. Unlike previously reported results for this data set, our fits do not exceed the limitations set by the nature of the data itself.

  3. 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

  4. 2D- and 3D-QSAR studies of a series of benzopyranes and benzopyrano[3,4b][1,4]-oxazines as inhibitors of the multidrug transporter P-glycoprotein

    NASA Astrophysics Data System (ADS)

    Jabeen, Ishrat; Wetwitayaklung, Penpun; Chiba, Peter; Pastor, Manuel; Ecker, Gerhard F.

    2013-02-01

    The ATP-binding cassette efflux transporter P-glycoprotein (P-gp) is notorious for contributing to multidrug resistance in antitumor therapy. Due to its expression in many blood-organ barriers, it also influences the pharmacokinetics of drugs and drug candidates and is involved in drug/drug- and drug/nutrient interactions. However, due to lack of structural information the molecular basis of ligand/transporter interaction still needs to be elucidated. Towards this goal, a series of Benzopyranes and Benzopyrano[3,4b][1,4]oxazines have been synthesized and pharmacologically tested for their ability to inhibit P-gp mediated daunomycin efflux. Both quantitative structure-activity relationship (QSAR) models using simple physicochemical and novel GRID-independent molecular descriptors (GRIND) were established to shed light on the structural requirements for high P-gp inhibitory activity. The results from 2D-QSAR showed a linear correlation of vdW surface area (Å2) of hydrophobic atoms with the pharmacological activity. GRIND (3D-QSAR) studies allowed to identify important mutual distances between pharmacophoric features, which include one H-bond donor, two H-bond acceptors and two hydrophobic groups as well as their distances from different steric hot spots of the molecules. Activity of the compounds particularly increases with increase of the distance of an H-bond donor or a hydrophobic feature from a particular steric hot spot of the benzopyrane analogs.

  5. Computational identification of novel histone deacetylase inhibitors by docking based QSAR.

    PubMed

    Nair, Syam B; Teli, Mahesh Kumar; Pradeep, H; Rajanikant, G K

    2012-06-01

    Histone deacetylases (HDACs) are enzymes that modify chromatin structure and contribute to aberrant gene expression in cancer. A series compounds with well-assigned HDAC inhibitory activity was used for docking based 3D-QSAR analysis. The 3D-QSAR acquired had excellent correlation coefficient value (q2=0.753) and high Fisher ratio (F=300.2). A validated pharmacophore model (AAAPR) was employed for virtual screening. After manual selection, molecular docking and further refinement, six compounds with good absorption, distribution, metabolism, and excretion (ADME) properties were selected as potential HDAC inhibitors. Further, the molecular interactions of these inhibitors with the HDAC active site residues were discussed in detail.

  6. Structural Antitumoral Activity Relationships of Synthetic Chalcones

    PubMed Central

    Echeverria, Cesar; Santibañez, Juan Francisco; Donoso-Tauda, Oscar; Escobar, Carlos A.; Ramirez-Tagle, Rodrigo

    2009-01-01

    Relationships between the structural characteristic of synthetic chalcones and their antitumoral activity were studied. Treatment of HepG2 cells for 24 h with synthetic 2’-hydroxychalcones resulted in apoptosis induction and dose-dependent inhibition of cell proliferation. The calculated reactivity indexes and the adiabatic electron affinities using the DFT method including solvent effects, suggest a structure-activity relationship between the Chalcones structure and the apoptosis in HepG2 cells. The absence of methoxy substituents in the B ring of synthetic 2’-hydroxychalcones, showed the mayor structure-activity pattern along the series. PMID:19333443

  7. Reaction of vascular adhesion protein-1 (VAP-1) with primary amines: mechanistic insights from isotope effects and quantitative structure-activity relationships.

    PubMed

    Heuts, Dominic P H M; Gummadova, Jennet O; Pang, Jiayun; Rigby, Stephen E J; Scrutton, Nigel S

    2011-08-26

    Human vascular adhesion protein-1 (VAP-1) is an endothelial copper-dependent amine oxidase involved in the recruitment and extravasation of leukocytes at sites of inflammation. VAP-1 is an important therapeutic target for several pathological conditions. We expressed soluble VAP-1 in HEK293 EBNA1 cells at levels suitable for detailed mechanistic studies with model substrates. Using the model substrate benzylamine, we analyzed the steady-state kinetic parameters of VAP-1 as a function of solution pH. We found two macroscopic pK(a) values that defined a bell-shaped plot of turnover number k(cat,app) as a function of pH, representing ionizable groups in the enzyme-substrate complex. The dependence of (k(cat)/K(m))(app) on pH revealed a single pK(a) value (∼9) that we assigned to ionization of the amine group in free benzylamine substrate. A kinetic isotope effect (KIE) of 6 to 7.6 on (k(cat)/K(m))(app) over the pH range of 6 to 10 was observed with d(2)-benzylamine. Over the same pH range, the KIE on k(cat) was found to be close to unity. The unusual KIE values on (k(cat)/K(m))(app) were rationalized using a mechanistic scheme that includes the possibility of multiple isotopically sensitive steps. We also report the analysis of quantitative structure-activity relationships (QSAR) using para-substituted protiated and deuterated phenylethylamines. With phenylethylamines we observed a large KIE on k(cat,app) (8.01 ± 0.28 with phenylethylamine), indicating that C-H bond breakage is limiting for 2,4,5-trihydroxyphenylalanine quinone reduction. Poor correlations were observed between steady-state rate constants and QSAR parameters. We show the importance of combining KIE, QSAR, and structural studies to gain insight into the complexity of the VAP-1 steady-state mechanism.

  8. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.

    PubMed

    Chen, Minjun; Hong, Huixiao; Fang, Hong; Kelly, Reagan; Zhou, Guangxu; Borlak, Jürgen; Tong, Weida

    2013-11-01

    Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administration-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR model's performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the model's applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compound's risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

  9. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors.

    PubMed

    Živković, Jelena V; Trutić, Nataša V; Veselinović, Jovana B; Nikolić, Goran M; Veselinović, Aleksandar M

    2015-09-01

    The Monte Carlo method was used for QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. The first QSAR model was developed for a series of 74 3-anilino-4-arylmaleimide derivatives. The second QSAR model was developed for a series of 177 maleimide derivatives. QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Two splits have been examined: one split into the training and test set for the first QSAR model, and one split into the training, test and validation set for the second. The statistical quality of the developed model is very good. The calculated model for 3-anilino-4-arylmaleimide derivatives had following statistical parameters: r(2)=0.8617 for the training set; r(2)=0.8659, and r(m)(2)=0.7361 for the test set. The calculated model for maleimide derivatives had following statistical parameters: r(2)=0.9435, for the training, r(2)=0.9262 and r(m)(2)=0.8199 for the test and r(2)=0.8418, r(av)(m)(2)=0.7469 and ∆r(m)(2)=0.1476 for the validation set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential glycogen synthase kinase-3β inhibitors has been presented by using defined structural alerts.

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

    DTIC Science & Technology

    2009-01-01

    erties, such as log P, would aid in estimating a chemical’s environmental fate and toxicology when applied to QSAR modeling. Granted, QSAR mod- els, such...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

  11. Synthesis, quantitative structure-activity relationship and biological evaluation of 1,3,4-oxadiazole derivatives possessing diphenylamine moiety as potential anticancer agents.

    PubMed

    Abdel Rahman, Doaa Ezzat

    2013-01-01

    Synthesis of 2,5-disubstituted-1,3,4-oxadiazole (2a-c), 3-substituted aminomethyl-5-substituted-1,3,4-oxadiazole-2(3H)-thione (4a-m) and 2-substituted thio-5-substituted-1,3,4-oxadiazole (5a, b) had been described. All the synthesized derivatives were screened for anticancer activity against HT29 and MCF7 cancer cell lines using Sulfo-Rodamine B (SRB) standard method. Most of the tested compounds exploited potent antiproliferative activity against HT29 cancer cell line rather than MCF7 cancer cell line. Compounds 2a-c, 4f and 5a exhibited potent cytotoxicity (IC(50) 1.3-2.0 µM) and selectivity against HT29 cancer cell line. Quantitative structure-activity relationship (QSAR) study was applied to find a correlation between the experimental antiproliferative activities of the newly synthesized oxadiazole derivatives with their physicochemical parameter and topological index.

  12. Docking and quantitative structure-activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase.

    PubMed

    Caballero, Julio; Vergara-Jaque, Ariela; Fernández, Michael; Coll, Deysma

    2009-11-01

    We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc) to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC(50)) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure-activity relationship (QSAR) method using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters [cross-validate correlation coefficient (Q(2) = 0.796) and fitted correlation coefficient (R(2) = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted four compounds that were not included in the training set.

  13. 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.

  14. QSAR Analysis for Some 1, 2-Benzisothiazol-3-one Derivatives as Caspase-3 Inhibitors by Stepwise MLR Method

    PubMed Central

    Hajimahdi, Zahra; Safizadeh, Fatemeh; Zarghi, Afshin

    2016-01-01

    Caspase-3 inhibitory activities of some 1, 2-benzisothiazol-3-one derivatives were modeled by quantitative structure–activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R2) of 0.91 and 0.59 for training and test groups, respectively. The quality of the model was evaluated by leave-one out (LOO) cross validation (LOO correlation coefficient, Q2) of 0.80). The results indicate that the descriptors related to the electronegativity, the atomic masses, the atomic van der Waals volumes and R--CX--R Atom-centered fragments play a more significant role in caspase-3 inhibitory activity. PMID:27642314

  15. On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials.

    PubMed

    Ghosh, Payel; Thanadath, Megha; Bagchi, Manish C

    2006-08-01

    The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade and as a result of that, fluoroquinolone drugs are being used as the second line of action. But there is hardly any study to examine specific structure activity relationships of quinolone antibacterials against mycobacteria. In this paper, an attempt has been made to establish a quantitative structure activity relationship modeling for a series of quinolone compounds against Mycobacterium fortuitum and Mycobacterium smegmatis. Due to lack of sufficient physicochemical data for the anti-mycobacterial compounds, it becomes very difficult to develop predictive methods based on experimental data. The present paper is an effort for the development of QSARs from the standpoint of physicochemical, constitutional, geometrical, electrostatic and topological indices. Molecular descriptors have been calculated solely from the chemical structure of N-1, C-7 and 8 substituted quinolone compounds and ridge regression models have been developed which can explain a better structure-activity relationship. Consideration of an intermolecular similarity analysis approach that led to a successful computer program development in PERL language has been used for comparing the influence of various molecular descriptors in different data subsets. The comparison of relative effectiveness of the calculated descriptors in our ridge regression model gives rise to some interesting results.

  16. 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 (BOD5) and chemical (COD) oxygen demand values determined for parent compounds ((BOD5/COD)0), as well as for their reaction mixtures in half-life achieved by UV-C/H2O2 process ((BOD5/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 (BOD5/COD)0 and (BOD5/COD)t1/2, respectively, according to the values of statistical parameters R(2) and Q(2). Hence, 3-variable model predicting (BOD5/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 (BOD5/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/H2O2.

  17. Theoretical molecular descriptors relevant to the uptake of persistent organic pollutants from soil by zucchini. A QSAR study.

    PubMed

    Bordás, Barna; Bélai, Iván; Koomíves, Tamás

    2011-04-13

    The uptake of persistent organic pollutants (POPs) from soil by plants allows the development of phytoremediation protocols to rehabilitate contaminated areas. The use of diverse theoretical descriptors has been reported in the literature for developing quantitative structure-activity relationship (QSAR) models for predicting the bioconcentration factors (BCFs) of POPs in different plants. In this paper an evaluation is given on the molecular properties of POPs in terms of theoretical molecular descriptors that are relevant to the uptake and accumulation of these persistent pollutants from soil by two zucchini varieties. Statistically significant and predictive linear regression models have been developed for the BCF values of 20 polychlorinated dibenzo-p-dioxins/dibenzofurans and 14 polyhalogenated biphenyls in two zucchini varieties based on retrospective data. The relevant parameters have been selected from a set of 1660 DRAGON, 150 VolSurf, and 11 quantum chemical descriptors. The two most significant regression models, containing VolSurf, DRAGON GETAWAY, and quantum chemical descriptors, displayed the following statistical parameters: (eq 3) n = 27, R(2) = 0.940, q(2) = 0.922, SE = 0.155, F = 392.1; (eq 4) n = 27, R(2) = 0.921, q(2) = 0.898, SE = 0.161, F = 140.4. Predictive capabilities of the equations have been validated by using external validation sets. The QSAR models proposed might contribute to the development of viable soil remediation strategies.

  18. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data.

    PubMed

    Toropova, Alla P; Toropov, Andrey A

    2017-03-07

    The prediction of biochemical endpoints is an important task of the modern medicinal chemistry, cell biology, and nanotechnology. Simplified molecular input-line entry system (SMILES) is a tool for representation of the molecular structure. In particular, SMILES can be used to build up the quantitative structure - property/activity relationships (QSPRs/QSARs). The QSPR/QSAR is a tool to predict an endpoint for a new substance, which has not been examined in experiment. Quasi-SMILES are representation of eclectic data related to an endpoint. In contrast to traditional SMILES, which are representation of the molecular structure, the quasi-SMILES are representation of conditions (in principle, the molecular structure also can be taken into account in quasi-SMILES). In this work, the quasi-SMILES were used to build up model for cell viability under impact of the metal-oxides nanoparticles by means of the CORAL software (http://www.insilico.eu/coral). The eclectic data for the quasi-SMILES are (i) molecular structure of metals-oxides; (ii) concentration of the nanoparticles; and (iii) the size of nanoparticles. The significance of different eclectic facts has been estimated. Mechanistic interpretation and the domain of applicability for the model are suggested. The statistical quality of the models is satisfactory for three different random distribution of available data into the training (sub-training and calibration) and the validation sets.

  19. (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.

  20. Quantitative structure-activity relationships for the toxicity of chlorophenols to mammalian submitochondrial particles.

    PubMed

    Argese, E; Bettiol, C; Giurin, G; Miana, P

    1999-04-01

    The toxicity of a series of chlorophenols, determined by a short-term in vitro assay utilizing mammalian submitochondrial particles, was related to the physicochemical and structural properties of these compounds. Quantitative Structure-Activity Relationships were defined by correlating EC50 values with six molecular descriptors, chosen to represent lipophilic, electronic and steric effects: the n-octanol/water partition coefficient (log Kow), the constant of Hammett (sigma sigma), the acid dissociation constant (pKa), the first order valence molecular connectivity index (1 chi v), the perimeter of the efficacious section (sigma D) and the melting point (m.p.). The results of regression analysis showed that log Kow is the most successful descriptor, indicating that the ability of chlorophenols to partition into the lipid bilayer of the mitochondrial membrane has an important role in determining their toxic effects. These results are consistent with a molecular mechanism of uncoupling action based on the chemiosmotic theory and on the protonophoric properties of chlorophenols. The quality of the QSAR models confirms the suitability of the SMP assay as a short-term prediction tool for aquatic toxicity of environmental pollutants acting on respiratory functions.

  1. QSAR study and VolSurf characterization of anti-HIV quinolone library

    NASA Astrophysics Data System (ADS)

    Filipponi, Enrica; Cruciani, Gabriele; Tabarrini, Oriana; Cecchetti, Violetta; Fravolini, Arnaldo

    2001-03-01

    Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.

  2. 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications.

    PubMed

    Ballante, Flavio; Ragno, Rino

    2012-06-25

    Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.

  3. Modeling Joint Effects of Mixtures of Chemicals on Microorganisms Using Quantitative Structure Activity Relationships

    DTIC Science & Technology

    1993-08-22

    toxicity results from the 40 chemicals placed in the testing set were used to develop QSAR models. Molecular connectivity indexes were calculated for...Toxic Unit, Additivity Index , and Mixture Toxicity Index . The validity of these concepts was further verified using the results of the 8-component testing...standard deviation of 22.6. These variations are comparable to those reported by Blum (1989) for activated sludge cultures and Microtox , and may be

  4. In silico study on β-aminoketone derivatives as thyroid hormone receptor inhibitors: a combined 3D-QSAR and molecular docking study.

    PubMed

    Wang, Fang-Fang; Yang, Wei; Shi, Yong-Hui; Le, Guo-Wei

    2016-12-01

    In order to explore the structure-activity correlation of a series of β-aminoketone analogs as inhibitors of thyroid hormone receptor (TR), a set of three-dimensional quantitative structure-activity relationship (3D-QSAR) models based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA), for the first time, were developed in the present work. The best CoMFA model with steric and electrostatic fields exhibited [Formula: see text], [Formula: see text] for TRβ, and [Formula: see text], [Formula: see text] for TRα. 3D contour maps produced from the optimal models were further analyzed individually, which provide the areas in space where interactive fields would affect the inhibitory activity. In addition, the binding modes of inhibitors at the active site of TRs were examined using molecular docking, the results indicated that this series of inhibitors fit into the active site of TRs by forming hydrogen bonding and electrostatic interactions. The docking studies also revealed that Leu305, Val458 for TRβ, and Asp407 for TRα are showing hydrogen bonds with the most active inhibitors. In any case, the 3D-QSAR models combined with the binding information will serve as a useful approach to explore the chemical space for improving the activity of TRβ and TRα inhibitors.

  5. Relationships between solar activity and climate change

    NASA Technical Reports Server (NTRS)

    Roberts, W. O.

    1975-01-01

    The relationship between recurrent droughts in the High Plains of the United States and the double sunspot cycle is discussed in detail. It is suggested that high solar activity is generally related to an increase in meridional circulation and blocking patterns at high and intermediate latitudes, especially in winter, and the effect is related to the sudden formation of cirrus clouds during strong geomagnetic activity that originates in the solar corpuscular emission.

  6. Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs.

    PubMed

    Kar, Supratik; Roy, Kunal

    2011-04-01

    Carcinogenicity is one of the toxicological endpoints causing the highest concern. Also, the standard bioassays in rodents used to assess the carcinogenic potential of chemicals and drugs are extremely long, costly and require the sacrifice of large numbers of animals. For these reasons, we have attempted development of a global quantitative structure-activity relationship (QSAR) model using a data set of 1464 compounds (the Galvez data set available from http://www.uv.es/-galvez/tablevi.pdf), including many marketed drugs for their carcinogenesis potential. Though experimental toxicity testing using animal models is unavoidable for new drug candidates at an advanced stage of drug development, yet the developed global QSAR model can in silico predict the carcinogenicity of new drug compounds to provide a tool for initial screening of new drug candidate molecules with reduced number of animal testing, money and time. Considering large number of data points with diverse structural features used for model development (n(training) = 732) and model validation (n(test) = 732), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.731, R2pred = 0.716). Our developed model suggests that higher lipophilicity values and conjugated ring systems, thioketo and nitro groups contribute positively towards drug carcinogenicity. On the contrary, tertiary and secondary nitrogens, phenolic, enolic and carboxylic OH fragments and presence of three-membered rings reduce the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-induced carcinogenicity. One may consider all these points to reduce carcinogenic potential of the molecules.

  7. Per- and polyfluoro toxicity (LC(50) inhalation) study in rat and mouse using QSAR modeling.

    PubMed

    Bhhatarai, Barun; Gramatica, Paola

    2010-03-15

    Fully or partially fluorinated compounds, known as per- and polyfluorinated chemicals are widely distributed in the environment and released because of their use in different household and industrial products. Few of these long chain per- and polyfluorinated chemicals are classified as emerging pollutants, and their environmental and toxicological effects are unveiled in the literature. This has diverted the production of long chain compounds, considered as more toxic, to short chains, but concerns regarding the toxicity of both types of per- and polyfluorinated chemicals are alarming. There are few experimental data available on the environmental behavior and toxicity of these compounds, and moreover, toxicity profiles are found to be different for the types of animals and species used. Quantitative structure-activity relationship (QSAR) is applied to a combination of short and long chain per- and polyfluorinated chemicals, for the first time, to model and predict the toxicity on two species of rodents, rat (Rattus) and mouse (Mus), by modeling inhalation (LC(50)) data. Multiple linear regression (MLR) models using the ordinary-least-squares (OLS) method, based on theoretical molecular descriptors selected by genetic algorithm (GA), were used for QSAR studies. Training and prediction sets were prepared a priori, and these sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the model was verified on a larger set of per- and polyfluorinated chemicals retrieved from different databases and journals. The descriptors involved, the similarities, and the differences observed between models pertaining to the toxicity related to the two species are discussed. Chemometric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) were used to select most toxic compounds from those within the AD of both models, which will be subjected to experimental tests

  8. Quantitative structure-activity relationships predicting the antioxidant potency of 17β-estradiol-related polycyclic phenols to inhibit lipid peroxidation.

    PubMed

    Prokai, Laszlo; Rivera-Portalatin, Nilka M; Prokai-Tatrai, Katalin

    2013-01-11

    The antioxidant potency of 17β-estradiol and related polycyclic phenols has been well established. This property is an important component of the complex events by which these types of agents are capable to protect neurons against the detrimental consequences of oxidative stress. In order to relate their molecular structure and properties with their capacity to inhibit lipid peroxidation, a marker of oxidative stress, quantitative structure-activity relationship (QSAR) studies were conducted. The inhibition of Fe3+-induced lipid peroxidation in rat brain homogenate, measured through an assay detecting thiobarbituric acid reactive substances for about seventy compounds were correlated with various molecular descriptors. We found that lipophilicity (modeled by the logarithm of the n-octanol/water partition coefficient, logP) was the property that influenced most profoundly the potency of these compounds to inhibit lipid peroxidation in the biological medium studied. Additionally, the important contribution of the bond dissociation enthalpy of the phenolic O-H group, a shape index, the solvent-accessible surface area and the energy required to remove an electron from the highest occupied molecular orbital were also confirmed. Several QSAR equations were validated as potentially useful exploratory tools for identifying or designing novel phenolic antioxidants incorporating the structural backbone of 17β-estradiol to assist therapy development against oxidative stress-associated neurodegeneration.

  9. Quantitative Structure-Activity Relationships Predicting the Antioxidant Potency of 17β-Estradiol-Related Polycyclic Phenols to Inhibit Lipid Peroxidation

    PubMed Central

    Prokai, Laszlo; Rivera-Portalatin, Nilka M.; Prokai-Tatrai, Katalin

    2013-01-01

    The antioxidant potency of 17β-estradiol and related polycyclic phenols has been well established. This property is an important component of the complex events by which these types of agents are capable to protect neurons against the detrimental consequences of oxidative stress. In order to relate their molecular structure and properties with their capacity to inhibit lipid peroxidation, a marker of oxidative stress, quantitative structure-activity relationship (QSAR) studies were conducted. The inhibition of Fe3+-induced lipid peroxidation in rat brain homogenate, measured through an assay detecting thiobarbituric acid reactive substances for about seventy compounds were correlated with various molecular descriptors. We found that lipophilicity (modeled by the logarithm of the n-octanol/water partition coefficient, logP) was the property that influenced most profoundly the potency of these compounds to inhibit lipid peroxidation in the biological medium studied. Additionally, the important contribution of the bond dissociation enthalpy of the phenolic O–H group, a shape index, the solvent-accessible surface area and the energy required to remove an electron from the highest occupied molecular orbital were also confirmed. Several QSAR equations were validated as potentially useful exploratory tools for identifying or designing novel phenolic antioxidants incorporating the structural backbone of 17β-estradiol to assist therapy development against oxidative stress-associated neurodegeneration. PMID:23344051

  10. Quantitative structure-activity relationship and molecular docking revealed a potency of anti-hepatitis C virus drugs against human corona viruses.

    PubMed

    Elfiky, Abdo A; Mahdy, Samah M; Elshemey, Wael M

    2016-11-19

    A number of human coronaviruses (HCoVs) were reported in the last and present centuries. Some outbreaks of which (eg, SARS and MERS CoVs) caused the mortality of hundreds of people worldwide. The problem of finding a potent drug against HCoV strains lies in the inability of finding a drug that stops the viral replication through inhibiting its important proteins. In spite of its limited efficacy and potential side effects, Ribavirin is extensively used as a first choice against HCoVs. Therefore, scientists reverted towards the investigation of different drugs that can more specifically target proteins. In this study, four anti-HCV drugs (one approved by FDA and others under clinical trials) are tested against HCoV polymerases. Quantitative Structure-Activity Relationship (QSAR) and molecular docking are both used to compare the performance of the selected nucleotide inhibitors to their parent nucleotides and Ribavirin. Both QSAR and molecular docking showed that IDX-184 is superior compared to Ribavirin against MERS CoV, a result that was also reported for HCV. MK-0608 showed a performance that is comparable to Ribavirin. We strongly suggest an in vitro study on the potency of these two drugs against MERS CoV.

  11. An orientation sensitive approach in biomolecule interaction quantitative structure-activity relationship modeling and its application in ion-exchange chromatography.

    PubMed

    Kittelmann, Jörg; Lang, Katharina M H; Ottens, Marcel; Hubbuch, Jürgen

    2017-01-27

    Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases.

  12. Quantitative structure-activity studies of octopaminergic agonists and antagonists against nervous system of Locusta migratoria.

    PubMed

    Hirashima, A; Pan, C; Shinkai, K; Tomita, J; Kuwano, E; Taniguchi, E; Eto, M

    1998-07-01

    The quantitative structure activity relationship (QSAR) of octopaminergic agonists and antagonists against the thoracic nerve cord of the migratory locust, Locusta migratoria L., was analyzed using physicochemical parameters and regression analysis. The hydrophobic effect, dipole moment, and shape index were important in terms of Ki: the more hydrophobic, the greater dipole moment, and the smaller shape index of the molecules, the greater the activity. A receptor surface model (RSM) was generated using some subset of the most active structures. Three-dimensional energetics descriptors were calculated from RSM/ligand interaction and these three-dimensional descriptors were used in QSAR analysis. This data set was studied further using molecular shape analysis.

  13. Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR

    PubMed Central

    Quesada-Romero, Luisa; Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2014-01-01

    Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q2 value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. PMID:25010341

  14. Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR.

    PubMed

    Quesada-Romero, Luisa; Mena-Ulecia, Karel; Tiznado, William; Caballero, Julio

    2014-01-01

    Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure-activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure-activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q(2) value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors.

  15. Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach.

    PubMed

    Gupta, Pawan; Garg, Prabha; Roy, Nilanjan

    2012-05-01

    The objective of this study is to identify novel HIV-1 integrase (IN) inhibitors. Here, shape-based screening and QSAR have been successfully implemented to identify the novel inhibitors for HIV-1 IN, and in silico validation is performed by docking studies. The 2D QSAR model of benzodithiazine derivatives was built using genetic function approximation (GFA) method with good internal (cross-validated r(2)  = 0.852) and external prediction (). Best docking pose of highly active molecule of the benzodithiazine derivatives was used as a template for shape-based screening of ZINC database. Toxicity prediction was also performed using Deductive Estimation of Risk from Existing Knowledge (DEREK) program to filter non-toxic molecules. Inhibitory activities of screened non-toxic molecules were predicted using derived QSAR models. Active, non-toxic screened molecules were also docked into the active site of HIV-1 IN using AutoDock and dock program. Some molecules docked similarly as highly active molecule of the benzodithiazine derivatives. These molecules also followed the same docking interactions in both the programs. Finally, four benzodithiazine derivatives were identified as novel HIV-1 integrase inhibitors based on QSAR predictions and docking interactions. ADME properties of these molecules were also computed using Discovery Studio.

  16. Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

    PubMed

    Lee, Sehan; Barron, Mace G

    2015-11-01

    Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs.

  17. Investigation on quantitative structure activity relationships and pharmacophore modeling of a series of mGluR2 antagonists.

    PubMed

    Zhang, Meng-Qi; Zhang, Xiao-Le; Li, Yan; Fan, Wen-Jia; Wang, Yong-Hua; Hao, Ming; Zhang, Shu-Wei; Ai, Chun-Zhi

    2011-01-01

    MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson's disease and schizophrenia. Herein, we report the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of a series of 1,3-dihydrobenzo[ b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [(3)H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q(2) of 0.513, R(2) (ncv) of 0.868, R(2) (pred) = 0.876, while the CoMSIA model yielded a Q(2) of 0.450, R(2) (ncv) = 0.899, R(2) (pred) = 0.735. For activity II study, CoMFA model yielded statistics of Q(2) = 0.5, R(2) (ncv) = 0.715, R(2) (pred) = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R(7), R(3) and position A benefit activity I of the antagonists, but decrease it when projected in R(8) and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity

  18. 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 (Kpew). For chemicals with the octanol-water partition coefficient (log Kow) <8, a TLSER model with Vx (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 KOW>8, a TLSER model with Vx 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.

  19. Correlating metal ionic characteristics with biosorption capacity using QSAR model.

    PubMed

    Can, Chen; Jianlong, Wang

    2007-11-01

    The relationship between metal ionic characteristics and the maximum biosorption capacity (q(max)) was established using QSAR model based on the classification of metal ions (soft, hard and borderline ions). Ten kinds of metal ions (Ag(+), Cs(+), Zn(2+), Pb(2+), N(i2+), Cu(2+), Co(2+), Sr(2+), Cd(2+), Cr(3+)) were selected and the waste biomass of Saccharomyces cerevisiae obtained from a local brewery was used as biosorbent. Eighteen parameters of physiochemical characteristics of metal ions were selected and correlated with q(max). Classification of metal ions could improve the QSAR models and different characteristics were significant in correlating with q(max), such as polarizing power Z(2)/r or the first hydrolysis constant |logK(OH)| or ionization potential IP. X(m)(2)r seemed to be suitable for metal ions including soft ions, and Z(2)/r, |logK(OH)| and IP suitable for only soft ions or metal ions excluding soft ions. It provided a new way to predict the biosorptive capacity of metal ions.

  20. QSAR study of selective ligands for the thyroid hormone receptor beta.

    PubMed

    Liu, Huanxiang; Gramatica, Paola

    2007-08-01

    In this paper, an accurate and reliable QSAR model of 87 selective ligands for the thyroid hormone receptor beta 1 (TRbeta1) was developed, based on theoretical molecular descriptors to predict the binding affinity of compounds with receptor. The structural characteristics of compounds were described wholly by a large amount of molecular structural descriptors calculated by DRAGON. Six most relevant structural descriptors to the studied activity were selected as the inputs of QSAR model by a robust optimization algorithm Genetic Algorithm. The built model was fully assessed by various validation methods, including internal and external validation, Y-randomization test, chemical applicability domain, and all the validations indicate that the QSAR model we proposed is robust and satisfactory. Thus, the built QSAR model can be used to fast and accurately predict the binding affinity of compounds (in the defined applicability domain) to TRbeta1. At the same time, the model proposed could also identify and provide some insight into what structural features are related to the biological activity of these compounds and provide some instruction for further designing the new selective ligands for TRbeta1 with high activity.

  1. 3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds.

    PubMed

    Caballero, Julio

    2010-11-01

    Inhibitory activities of flavonoid derivatives against aldose reductase (AR) enzyme were modelled by using CoMFA, CoMSIA and GALAHAD methods. CoMFA and CoMSIA methods were used for deriving quantitative structure-activity relationship (QSAR) models. All QSAR models were trained with 55 compounds, after which they were evaluated for predictive ability with additional 14 compounds. The best CoMFA model included both steric and electrostatic fields, meanwhile, the best CoMSIA model included steric, hydrophobic and H-bond acceptor fields. These models had a good predictive quality according to both internal and external validation criteria. On the other hand, GALAHAD was used for deriving a 3D pharmacophore model. Twelve active compounds were used for deriving this model. The obtained model included hydrophobe, hydrogen bond acceptor and hydrogen bond donor features; it was able to identify the active AR inhibitors from the remaining compounds. These in silico tools might be useful in the rational design of new AR inhibitors.

  2. 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.

  3. 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.

  4. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Artemenko, A. G.; Muratov, Eugene N.; Polischuk, P. G.; Ognichenko, L. N.; Liahovsky, A. V.; Hromov, A. I.; Varlamova, E. V.

    This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it's a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the "molecular alignment" problem, consideration of different physical-chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D-4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the "HiT QSAR" software that so includes powerful statistical capabilities and a number of useful utilities.

  5. Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm.

    PubMed

    Zaheer-ul, Haq; Uddin, Reaz; Yuan, Hongbin; Petukhov, Pavel A; Choudhary, M Iqbal; Madura, Jeffry D

    2008-05-01

    Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.

  6. Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin

    NASA Astrophysics Data System (ADS)

    Hussain, Abrar; Melville, James L.; Hirst, Jonathan D.

    2010-01-01

    Actin-binding natural products have been identified as a potential basis for the design of cancer therapeutic agents. We report flexible docking and QSAR studies on aplyronine A analogues. Our findings show the macrolide `tail' to be fundamental for the depolymerisation effect of actin-binding macrolides and that it is the tail which forms the initial interaction with the actin rather than the macrocycle, as previously believed. Docking energy scores for the compounds were highly correlated with actin depolymerisation activity. The 3D-QSAR models were predictive, with the best model giving a q 2 value of 0.85 and a r 2 of 0.94. Results from the docking simulations and the interpretation from QSAR "coeff*stdev" contour maps provide insight into the binding mechanism of each analogue and highlight key features that influence depolymerisation activity. The results herein may aid the design of a putative set of analogues that can help produce efficacious and tolerable anti-tumour agents. Finally, using the best QSAR model, we have also made genuine predictions for an independent set of recently reported aplyronine analogues.

  7. A Systematic Investigation of Quaternary Ammonium Ions as Asymmetric Phase Transfer Catalysts. Application of Quantitative Structure Activity/Selectivity Relationships

    PubMed Central

    Denmark, Scott E.; Gould, Nathan D.; Wolf, Larry M.

    2011-01-01

    While the synthetic utility of asymmetric phase transfer catalysis continues to expand, the number of proven catalyst types and design criteria remains limited. At the origin of this scarcity is a lack in understanding of how catalyst structural features affect the rate and enantioselectivity of phase transfer catalyzed reactions. Described in this paper is the development of quantitative structure-activity relationships (QSAR) and -selectivity relationships (QSSR) for the alkylation of a protected glycine imine with libraries of quaternary ammonium ion catalysts. Catalyst descriptors including ammonium ion accessibility, interfacial adsorption affinity, and partition coefficient were found to correlate meaningfully with catalyst activity. The physical nature of the descriptors was rationalized through differing contributions of the interfacial and extraction mechanisms to the reaction under study. The variation in the observed enantioselectivity was rationalized employing a comparative molecular field analysis (CoMFA) using both the steric and electrostatic fields of the catalysts. A qualitative analysis of the developed model reveals preferred regions for catalyst binding to afford both configurations of the alkylated product. PMID:21446723

  8. Mechanistic QSAR models for interpreting degradation rates of sulfonamides in UV-photocatalysis systems.

    PubMed

    Huang, Xiangfeng; Feng, Yi; Hu, Cui; Xiao, Xiaoyu; Yu, Daliang; Zou, Xiaoming

    2015-11-01

    Photocatalysis is one of the most effective methods for treating antibiotic wastewater. Thus, it is of great significance to determine the relationship between degradation rates and structural characteristics of antibiotics in photocatalysis processes. In the present study, the photocatalytic degradation characteristics of 10 sulfonamides (SAs) were studied using two photocatalytic systems composed of nanophase titanium dioxide (nTiO2) plus ultraviolet (UV) and nTiO2/activated carbon fiber (ACF) plus UV. The results indicated that the largest apparent SA degradation rate constant (Kapp) is approximately 5 times as large as that of the smallest one. Based on the degradation mechanism and the partial least squares regression (PLS) method, optimum Quantitative Structure Activity Relationship (QSAR) models were developed for the two systems. Mechanistic models indicated that the degradation rule of SAs in the TiO2 systems strongly relates to their highest occupied molecular orbital (Ehomo), the maximum values of nucleophilic attack (f(+)x), and the minimum values of the most negative partial charge on a main-chain atom (q(C)min), whereas the maximum values of OH radical attack (f(0)x) and the apparent adsorption rate constant values (kad) are key factors affecting the degradation rule of SAs in the TiO2/ACF system.

  9. QSAR study of the toxicity of nitrobenzenes to river bacteria and photobacterium phosphoreum

    SciTech Connect

    Yuan, X.; Lu, G.; Lang, P.

    1997-01-01

    Since nitrobenzenes constitute a class of industrial chemicals that are present in Songhua River and probably in many other industrialized countries as well, it is useful to gain insight into their potential hazard to aquatic organisms. For this reason, it was decided to determine data on the toxicity for bacteria in the Songhua River. Furthermore, the toxicity to Ph. phosphoreum was determined in the Microtox assay, in order to further evaluate the usefulness of this assay for hazard assessment. Quantitative structure-activity relationships (QSARs) have been developed for aromatic nitro compound toxicity to aquatic species, but no data on the toxicity of nitrobenzenes to environmental bacteria were used. In this study, the toxicity of various substituted nitrobenzenes to bacteria in Songhua River and to Ph. phosphoreum has been investigated, establishing quantitative structure-activity relationships with n-octanol-water partition coefficient (log P), the energy of the lowest unoccupied molecular orbital (E{sub LUMO}) and the sum of substituent constant ({Sigma}{sigma}-). 12 refs., 2 tabs.

  10. Understanding the Molecular Determinant of Reversible Human Monoamine Oxidase B Inhibitors Containing 2H-chromen-2-One Core: Structure-Based and Ligand-Based Derived 3-D QSAR Predictive Models.

    PubMed

    Mladenovic, Milan; Patsilinakos, Alexandros; Pirolli, Adele; Sabatino, Manuela; Ragno, Rino

    2017-03-14

    Monoamine oxidase B (MAO B) catalyzes the oxidative deamination of aryalkylamines neurotransmitters with concomitant reduction of oxygen to hydrogen peroxide. Consequently, the enzyme's malfunction can induce oxidative damage to mitochondrial DNA and mediates development of Parkinson's disease. Thus, MAO B emerges as a promising target for developing pharmaceuticals potentially useful to treat this vicious neurodegenerative condition. Aiming to contribute to the development of drugs with the reversible mechanism of MAO B inhibition only, herein, an extended in silico-in vitro procedure for the selection of novel MAO B inhibitors is demonstrated, including: (1) definition of optimized and validated structure-based (SB) 3-D QSAR models derived from available co-crystallized inhibitor-MAO B complexes; (2) elaboration of structure-activity relationships (SAR) features for either irreversible or reversible MAO B inhibitors to characterize and improve coumarin-based inhibitor activity (Protein Data Bank ID: 2V61) as the most potent reversible lead compound; (3) definition of structure-based (SB) and ligand-based (LB) alignment rules assessments by which virtually any untested potential MAO B inhibitor might be evaluated; (4) predictive ability validation of the best 3-D QSAR model through SB/LB modeling of four coumarin-based external test sets (267 compounds); (5) design and SB/LB alignment of novel coumarin-based scaffolds experimentally validated through synthesis and biological evaluation in vitro. Due to the wide range of molecular diversity within the 3-D QSARs training set and derived features, the selected N probe-derived 3-D QSAR model proves to be a valuable tool for virtual screening (VS) of novel MAO B inhibitors and a platform for design, synthesis and evaluation of novel active structures. Accordingly, six highly active and selective MAO B inhibitors (picomolar to low nanomolar range of activity) were disclosed as a result of rational SB/LB 3-D QSAR design

  11. Relationship between potential platelet activation and LCS

    NASA Astrophysics Data System (ADS)

    Shadden, Shawn

    2010-11-01

    In the study of blood flow, emphasis is often directed at understanding shear stress at the vessel wall due to its potentially disruptive influence on the endothelium. However, it is also known that shear stress has a potent effect on platelet activation. Platelet activation is a precursor for blood clotting, which in turn is the cause of most forms of death. Since most platelets are contained in the flow domain, it is important to consider stresses acting on the platelet as they are convected. Locations of high stress can correspond to boundaries between different dynamic regions and locations of hyperbolic points in the Eulerian sense. In the computation of LCS, strain in typically considered in the Lagrangian sense. In this talk we discuss the relationship between locations of potential platelet activation due to increased stress and locations of LCS marking increase Lagrangian deformation.

  12. 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.

  13. Inhibition of /sup 125/I-labeled ristocetin binding to Micrococcus luteus cells by the peptides related to bacterial cell wall mucopeptide precursors: quantitative structure-activity relationships

    SciTech Connect

    Kim, K.H.; Martin, Y.; Otis, E.; Mao, J.

    1989-01-01

    Quantitative structure-activity relationships (QSAR) of N-Ac amino acids, N-Ac dipeptides, and N-Ac tripeptides in inhibition of /sup 125/I-labeled ristocetin binding to Micrococcus luteus cell wall have been developed to probe the details of the binding between ristocetin and N-acetylated peptides. The correlation equations indicate that (1) the binding is stronger for peptides in which the side chain of the C-terminal amino acid has a large molar refractivity (MR) value, (2) the binding is weaker for peptides with polar than for those with nonpolar C-terminal side chains, (3) the N-terminal amino acid in N-Ac dipeptides contributes 12 times that of the C-terminal amino acid to binding affinity, and (4) the interactions between ristocetin and the N-terminal amino acid of N-acetyl tripeptides appear to be much weaker than those with the first two amino acids.

  14. Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals.

    PubMed

    Speck-Planche, Alejandro; Kleandrova, Valeria V; Cordeiro, M N D S

    2013-05-15

    Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.

  15. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS.

    PubMed

    Gramatica, Paola; Cassani, Stefano; Chirico, Nicola

    2014-05-15

    A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.

  16. Structure-activity relationship study on the binding of PBDEs with thyroxine transport proteins.

    PubMed

    Yang, Weihua; Shen, Shide; Mu, Lailong; Yu, Hongxia

    2011-11-01

    Molecular docking and three-dimensional quantitative structure-activity relationships (3D-QSAR) were used to develop models to predict binding affinity of polybrominated diphenyl ether (PBDE) compounds to the human transthyretin (TTR). Based on the molecular conformations derived from the molecular docking, predictive comparative molecular similarity indices analysis (CoMSIA) models were developed. The results of CoMSIA models were as follows: leave-one-out (LOO) cross-validated squared coefficient q² (LOO) = 0.827 (full model, for all 28 compounds); q² (LOO) = 0.752 (split model, for 22 compounds in the training set); leave-many-out (LMO) cross-validated squared coefficient q² (LMO, two groups) = 0.723 ± 0.100 (full model, for all 28 compounds); q² (LMO, five groups) = 0.795 ± 0.030 (full model, for all 28 compounds); and the predictive squared correlation coefficient r²(pred)  = 0.928 (for six compounds in the test set). The developed CoMSIA models can be used to infer the activities of compounds with similar structural characteristics. In addition, the interaction mechanism between hydroxylated polybrominated diphenyl ethers (HO-PBDEs) and the TTR was explored. Hydrogen bonding with amino acid residues Asp74, Ala29, and Asn27 may be an important determinant for HO-PBDEs binding to TTR. Among them, forming hydrogen bonds with amino acid residues Asp74 might exert a more important function.

  17. 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).

  18. Peptide Bacteriocins--Structure Activity Relationships.

    PubMed

    Etayash, Hashem; Azmi, Sarfuddin; Dangeti, Ramana; Kaur, Kamaljit

    2015-01-01

    With the growing concerns in the scientific and health communities over increasing levels of antibiotic resistance, antimicrobial peptide bacteriocins have emerged as promising alternatives to conventional small molecule antibiotics. A substantial attention has recently focused on the utilization of bacteriocins in food preservation and health safety. Despite the fact that a large number of bacteriocins have been reported, only a few have been fully characterized and structurally elucidated. Since knowledge of the molecular structure is a key for understanding the mechanism of action and therapeutic effects of peptide, we centered our focus in this review on the structure-activity relationships of bacteriocins with a particular focus in seven bacteriocins, namely, nisin, microcin J25, microcin B17, microcin C, leucocin A, sakacin P, and pediocin PA-1. Significant structural changes responsible for the altered activity of the recent bacteriocin analogues are discussed here.

  19. 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

  20. QSAR analyses of DDT analogues and their in silico validation using molecular docking study against voltage-gated sodium channel of Anopheles funestus.

    PubMed

    Saini, V; Kumar, A

    2014-01-01

    DDT has enjoyed the reputation of a successful pesticide in disease control programme and agricultural practices along with the serious opposition and ban later on due to its biomagnification and toxic action against non-target organisms. The present work was carried out to develop QSAR models for analysing DDT analogues for their pesticidal activity and in silico validation of these models. A 2D-QSAR model was generated using stepwise with multiple regression, and the model with a value of r(2) = 0.7324; q(2) = 0.6215; pred r(2) = 0.7038, containing five descriptors, was selected for further study. The 3D QSAR with CoMFA analysis showed that steric contribution of 21% and electrostatic contribution of about 79% were required for larvicidal activity of DDT analogues. A set of 3430 molecules was generated using the basic DDT skeleton as template, and these were evaluated for their mosquito larvicidal activity using the generated QSAR models and DDT as standard. Eleven molecules were selected for in silico validation of these models. For this, a docking study of the selected molecules against the homology-modelled voltage-gated sodium channel of Anopheles funestus was conducted. The study showed that the activities of these analogues as predicted by 2D-QSAR, 3D-QSAR with CoMFA and dock score were observed to be well correlated.

  1. Structure-activity relationships of seco-prezizaane terpenoids in gamma-aminobutyric acid receptors of houseflies and rats.

    PubMed

    Kuriyama, Tadahiko; Schmidt, Thomas J; Okuyama, Emi; Ozoe, Yoshihisa

    2002-06-01

    Thirteen seco-prezizaane terpenoids isolated from star anise species (Illcium floridanum, Illcium parviflorum, and Illcium verum) were investigated for their ability to inhibit the specific binding of [(3)H]4'-ethynyl-4-n-propylbicycloorthobenzoate (EBOB), a non-competitive antagonist of gamma-aminobutyric acid (GABA) receptors, to housefly-head and rat-brain membranes. Veranisatin A was found to be the most potent inhibitor in both membranes, with an IC(50)(fly) of 78.5 nM and an IC(50)(rat) of 271 nM, followed by anisatin (IC(50)(fly)=123 nM; IC(50)(rat)=282 nM). Six of the other 11 tested compounds were effective only in housefly-head membranes. Pseudoanisatin proved to display a high (>26-fold) selectivity for housefly versus rat GABA receptors (IC(50)(fly)=376 nM; IC(50)(rat) >10,000 nM). Although pseudoanisatin does not structurally resemble EBOB, Scatchard plots indicated that the two compounds bind to the same site in housefly receptors. Anisatin and pseudoanisatin exhibited moderate insecticidal activity against German cockroaches. Comparative molecular field analysis (CoMFA), a method of three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, demonstrated that seco-prezizaane terpenoids can bind to the same site as do picrotoxane terpenoids such as picrotoxinin and picrodendrins, and the CoMFA maps allowed us to identify the parts of the molecules essential to high activity in housefly GABA receptors.

  2. Predictive three-dimensional quantitative structure-activity relationship of cytochrome P450 1A2 inhibitors.

    PubMed

    Korhonen, Laura E; Rahnasto, Minna; Mähönen, Niina J; Wittekindt, Carsten; Poso, Antti; Juvonen, Risto O; Raunio, Hannu

    2005-06-02

    The purpose of this study was to determine the cytochrome P450 1A2 (CYP1A2) inhibition potencies of structurally diverse compounds to create a comprehensive three-dimensional quantitative structure-activity relationship (3D-QSAR) model of CYP1A2 inhibitors and to use this model to predict the inhibition potencies of an external set of compounds. Fifty-two compounds including naphthalene, lactone and quinoline derivatives were assayed in a 96-well plate format for CYP1A2 inhibition activity using 7-ethoxyresorufin O-dealkylation as the probe reaction. The IC50 values of the tested compounds varied from 2.3 microM to over 40,000 microM. On the basis of this data set, a comparative molecular field analysis (CoMFA) and GRID/GOLPE models were created that yielded novel structural information about the interaction between inhibitory molecules and the CYP1A2 active site. The created CoMFA model was able to accurately predict inhibitory potencies of several structurally unrelated compounds, including selective inhibitors of other cytochrome P450 forms.

  3. 3D-QSAR and docking studies of pentacycloundecylamines at the sigma-1 (σ1) receptor.

    PubMed

    Geldenhuys, Werner J; Novotny, Nicholas; Malan, Sarel F; Van der Schyf, Cornelis J

    2013-03-15

    Pentacycloundecylamine (PCU) derived compounds have been shown to be promising lead structures for the development of novel drug candidates aimed at a variety of neurodegenerative and psychiatric diseases. Here we show for the first time a 3D quantitative structure-activity relationship (3D-QSAR) for a series of aza-PCU-derived compounds with activity at the sigma-1 (σ1) receptor. A comparative molecular field analysis (CoMFA) model was developed with a partial least squares cross validated (q(2)) regression value of 0.6, and a non-cross validated r(2) of 0.9. The CoMFA model was effective at predicting the sigma-1 activities of a test set with an r(2) >0.7. We also describe here the docking of the PCU-derived compounds into a homology model of the sigma-1 (σ1) receptor, which was developed to gain insight into binding of these cage compounds to the receptor. Based on docking studies we evaluated in a [(3)H]pentazocine binding assay an oxa-PCU, NGP1-01 (IC50=1.78μM) and its phenethyl derivative (IC50=1.54μM). Results from these studies can be used to develop new compounds with specific affinity for the sigma-1(σ1) receptor.

  4. Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening.

    PubMed

    Vrontaki, Eleni; Melagraki, Georgia; Mavromoustakos, Thomas; Afantitis, Antreas

    2016-01-01

    A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the "Thumb Pocket 2" allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.

  5. Aldose reductase inhibitors for diabetic complications: Receptor induced atom-based 3D-QSAR analysis, synthesis and biological evaluation.

    PubMed

    Vyas, Bhawna; Singh, Manjinder; Kaur, Maninder; Bahia, Malkeet Singh; Jaggi, Amteshwar Singh; Silakari, Om; Singh, Baldev

    2015-06-01

    Herein, atom-based 3D-QSAR analysis was performed using receptor-guided alignment of 46 flavonoid inhibitors of aldose reductase (ALR2) enzyme. 3D-QSAR models were generated in PHASE programme, and the best model corresponding to PLS factor four (QSAR4), was selected based on different statistical parameters (i.e., Rtrain(2), 0.96; Qtest(2) 0.81; SD, 0.26). The contour plots of different structural properties generated from the selected model were utilized for the designing of five new congener molecules. These designed molecules were duly synthesized, and evaluated for their in vitro ALR2 inhibitory activity that resulted in the micromolar (IC50<22μM) activity of all molecules. Thus, the newly designed molecules having ALR inhibitory potential could be employed for the management of diabetic complications.

  6. Synthesis and 3D-QSAR study of 1,4-dihydropyridine derivatives as MDR cancer reverters.

    PubMed

    Radadiya, Ashish; Khedkar, Vijay; Bavishi, Abhay; Vala, Hardevsinh; Thakrar, Shailesh; Bhavsar, Dhairya; Shah, Anamik; Coutinho, Evans

    2014-03-03

    A series of symmetrical and unsymmetrical 1,4-dihydropyridines were synthesized by a rapid, single pot microwave irradiation (MWI) based protocol along with conventional approach and characterized by NMR, IR and mass spectroscopic techniques. The compounds were evaluated for their tumor cell cytotoxicity in HL-60 tumor cells. A 3D-QSAR study using CoMFA and CoMSIA was carried out to decipher the factors governing MDR reversing ability in cancer. The resulting contour maps derived by the best 3D-QSAR models provide a good insight into the molecular features relevant to the biological activity in this series of analogs. 3D contour maps as a result of 3D-QSAR were utilized to identify some novel features that can be incorporated into the 1,4-dihydropyridine framework to enhance the activity.

  7. 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.

  8. Quantitative structure–activity relationship analysis of the pharmacology of para-substituted methcathinone analogues

    PubMed Central

    Bonano, J S; Banks, M L; Kolanos, R; Sakloth, F; Barnier, M L; Glennon, R A; Cozzi, N V; Partilla, J S; Baumann, M H; Negus, S S

    2015-01-01

    Background and Purpose Methcathinone (MCAT) is a potent monoamine releaser and parent compound to emerging drugs of abuse including mephedrone (4-CH3 MCAT), the para-methyl analogue of MCAT. This study examined quantitative structure–activity relationships (QSAR) for MCAT and six para-substituted MCAT analogues on (a) in vitro potency to promote monoamine release via dopamine and serotonin transporters (DAT and SERT, respectively), and (b) in vivo modulation of intracranial self-stimulation (ICSS), a behavioural procedure used to evaluate abuse potential. Neurochemical and behavioural effects were correlated with steric (Es), electronic (σp) and lipophilic (πp) parameters of the para substituents. Experimental Approach For neurochemical studies, drug effects on monoamine release through DAT and SERT were evaluated in rat brain synaptosomes. For behavioural studies, drug effects were tested in male Sprague-Dawley rats implanted with electrodes targeting the medial forebrain bundle and trained to lever-press for electrical brain stimulation. Key Results MCAT and all six para-substituted analogues increased monoamine release via DAT and SERT and dose- and time-dependently modulated ICSS. In vitro selectivity for DAT versus SERT correlated with in vivo efficacy to produce abuse-related ICSS facilitation. In addition, the Es values of the para substituents correlated with both selectivity for DAT versus SERT and magnitude of ICSS facilitation. Conclusions and Implications Selectivity for DAT versus SERT in vitro is a key determinant of abuse-related ICSS facilitation by these MCAT analogues, and steric aspects of the para substituent of the MCAT scaffold (indicated by Es) are key determinants of this selectivity. PMID:25438806

  9. Quantitative Structure Activity Relationship for Inhibition of Human Organic Cation/Carnitine Transporter (OCTN2)

    PubMed Central

    Diao, Lei; Ekins, Sean; Polli, James E.

    2010-01-01

    Organic cation/carnitine transporter (OCTN2; SLC22A5) is an important transporter for L-carnitine homeostasis, but can be inhibited by drugs, which may cause L-carnitine deficiency and possibly other OCTN2-mediated drug-drug interactions. One objective was to develop a quantitative structure–activity relationship (QSAR) of OCTN2 inhibitors, in order to predict and identify other potential OCTN2 inhibitors and infer potential clinical interactions. A second objective was to assess two high renal clearance drugs that interact with OCTN2 in vitro (cetirizine and cephaloridine) for possible OCTN2-mediated drug-drug interactions. Using previously generated in vitro data of 22 drugs, a 3D quantitative pharmacophore model and a Bayesian machine learning model were developed. The four pharmacophore features include two hydrophobic groups, one hydrogen-bond acceptor, and one positive ionizable center. The Bayesian machine learning model was developed using simple interpretable descriptors and function class fingerprints of maximum diameter 6 (FCFP_6). An external test set of 27 molecules, including 15 newly identified OCTN2 inhibitors, and a literature test set of 22 molecules were used to validate both models. The computational models afforded good capability to identify structurally diverse OCTN2 inhibitors, providing a valuable tool to predict new inhibitors efficiently. Inhibition results confirmed our previously observed association between rhabdomyolysis and Cmax/Ki ratio. The two high renal clearance drugs cetirizine and cephaloridine were found not to be OCTN2 substrates and their diminished elimination by other drugs is concluded not to be mediated by OCTN2. PMID:20831193

  10. Construction of 4D-QSAR models for use in the design of novel p38-MAPK inhibitors.

    PubMed

    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-alpha (TNF-alpha) 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 angstroms 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, Q2adj, ranged from 0.67 to 0.85. Model D (Q2adj = 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.

  11. 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.

  12. Study on the multiple mechanisms underlying the reaction between hydroxyl radical and phenolic compounds by qualitative structure and activity relationship.

    PubMed

    Cheng, Zhiyong; Ren, Jie; Li, Yuanzong; Chang, Wenbao; Chen, Zhida

    2002-12-01

    The activity-structure relationships (ASR) of phenolic compounds as hydroxyl-radical scavengers have mostly been studied and discussed with regard to their iron-chelating and hydrogen-donation properties in Fenton-type system, but extensive elucidation of multiple mechanisms underlying the hydroxyl radical scavenging reaction is out of obtaining up to now. In the present paper, a series of phenolic compounds was studied for their reactivity with hydroxyl radical by computed chemistry and deoxyribose degradation assay. The rate constant (K(S)), an index dependent markedly on the reaction mechanism and intrinsic reactivity of antioxidants, was found to have good correlation with hydroxyl O-H bond strength (DeltaH(f)), electron-donating ability (ionization potential approximated by HOMO energy level), enthalpy of single electron transfer (E(a)), and spin distribution of phenoxyl radicals (Ds(r)) after H-abstraction. Moreover, the theoretical parameters were highly intercorrelated, suggesting that multiple mechanisms co-exist in the hydroxyl-radical-scavenging reaction and interact with each other. Multi-linear regression analysis indicated that, in addition to H-atom transfer, electron transfer process and stability of the resulted phenoxyl radicals also significantly influence the reactivity of quenching hydroxyl radicals. The QSAR model so established here was based on the elucidation of the complex molecular mechanisms, and may reasonably predict the antioxidant activity using simple experimental and calculated parameters.

  13. 3D-QSAR studies on chromone derivatives as HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Ungwitayatorn, Jiraporn; Samee, Weerasak; Pimthon, Jutarat

    2004-02-01

    The three-dimensional quantitative structure-activity relationship (3D-QSAR) approach using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 30 chromone derivatives, a new class of HIV-1 protease inhibitors. The best predictive CoMFA model gives cross-validated r2 ( q2)=0.763, non-cross-validated r2=0.967, standard error of estimate ( S)=5.092, F=90.701. The best CoMSIA model has q2=0.707, non-cross-validated r2=0.943, S=7.018, F=51.734, included steric, electrostatic, hydrophobic, and hydrogen bond donor fields. The predictive ability of these models was validated by a set of five compounds that were not included in the training set. The calculated (predicted) and experimental inhibitory activities were well correlated. The contour maps obtained from CoMFA and CoMSIA models were in agreement with the previous docking study for this chromone series.

  14. Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array*

    PubMed Central

    Shinkai-Ouchi, Fumiko; Koyama, Suguru; Ono, Yasuko; Hata, Shoji; Ojima, Koichi; Shindo, Mayumi; duVerle, David; Ueno, Mika; Kitamura, Fujiko; Doi, Naoko; Takigawa, Ichigaku; Mamitsuka, Hiroshi; Sorimachi, Hiroyuki

    2016-01-01

    Calpains are intracellular Ca2+-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis. Thus, it is critically important to determine the site(s) in proteins at which calpains cleave. However, the calpains' substrate specificity remains unclear, because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains' substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10′ of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. The kcat/Kms for 119 sites ranged from 12.5–1,710 M−1s−1. Although most sites were cleaved by both calpain-1 and −2 with a similar kcat/Km, sequence comparisons revealed distinct aa preferences at P9-P7/P2/P5′. The aa compositions of the novel sites were not statistically different from those of previously reported sites as a whole, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is because of substrates' higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) were preferred for proteolysis over C-terminal (P′-sites). Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achieved kcat/Km prediction with r = 0.834, and binary-QSAR modeling attained an 87.5% positive prediction value for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3′, and P4′ sites, and P1-P2 cooperativity. Furthermore, using our

  15. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

    SciTech Connect

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    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 binding 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 cor