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Sample records for danish qsar database

  1. The Danish Collaborative Bacteraemia Network (DACOBAN) database.

    PubMed

    Gradel, Kim Oren; Schønheyder, Henrik Carl; Arpi, Magnus; Knudsen, Jenny Dahl; Ostergaard, Christian; Søgaard, Mette

    2014-01-01

    The Danish Collaborative Bacteraemia Network (DACOBAN) research database includes microbiological data obtained from positive blood cultures from a geographically and demographically well-defined population serviced by three clinical microbiology departments (1.7 million residents, 32% of the Danish population). The database also includes data on comorbidity from the Danish National Patient Registry, vital status from the Danish Civil Registration System, and clinical data on 31% of nonselected records in the database. Use of the unique civil registration number given to all Danish residents enables linkage to additional registries for specific research projects. The DACOBAN database is continuously updated, and it currently comprises 39,292 patients with 49,951 bacteremic episodes from 2000 through 2011. The database is part of an international network of population-based bacteremia registries from five developed countries on three continents. The main purpose of the DACOBAN database is to study surveillance, risk, and prognosis. Sex- and age-specific data on background populations enables the computation of incidence rates. In addition, the high number of patients facilitates studies of rare microorganisms. Thus far, studies on Staphylococcus aureus, enterococci, computer algorithms for the classification of bacteremic episodes, and prognosis and risk in relation to socioeconomic factors have been published.

  2. The Danish Microbiology Database (MiBa) 2010 to 2013.

    PubMed

    Voldstedlund, M; Haarh, M; Mølbak, K

    2014-01-09

    The Danish Microbiology Database (MiBa) is a national database that receives copies of reports from all Danish departments of clinical microbiology. The database was launched in order to provide healthcare personnel with nationwide access to microbiology reports and to enable real-time surveillance of communicable diseases and microorganisms. The establishment and management of MiBa has been a collaborative process among stakeholders, and the present paper summarises lessons learned from this nationwide endeavour which may be relevant to similar projects in the rapidly changing landscape of health informatics.

  3. Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

    PubMed Central

    Chavan, Swapnil; Nicholls, Ian A.; Karlsson, Björn C. G.; Rosengren, Annika M.; Ballabio, Davide; Consonni, Viviana; Todeschini, Roberto

    2014-01-01

    A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity. PMID:25302621

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

    PubMed Central

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

    2013-01-01

    Purpose Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Methods Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. Results & Conclusions QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71–100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles. PMID:23269503

  5. ASTER: An integration of the AQUIRE database and the QSAR system for use in ecological risk assessments

    SciTech Connect

    Russom, C.L.; Anderson, E.B.; Greenwood, B.E.; Pilli, A.

    1990-01-01

    Ecological risk assessments are used by the U.S. Environmental Protection Agency (USEPA) and other governmental agencies to assist in determining the probability and magnitude of deleterious effects of hazardous chemicals on plants and animals. These assessments are important steps in formulating regulatory decisions. The completion of an ecological risk assessment requires the gathering of ecotoxicological hazard and environmental exposure information. The information is evaluated in the risk characterization section to assist in making the final risk assessment. ASTER (Assessment Tools for the Evaluation of Risk) was designed by the USEPA Environmental Research Laboratory-Duluth (ERL-D) to assist regulators in producing risk assessments. ASTER is an integration of the AQUIRE (AQUatic toxicity Information Retrieval System) and QSAR (Quantitative Structure Activity Relationships) systems. AQUIRE is a database of aquatic toxicity tests and QSAR is comprised of a database of measured physicochemical properties, and various QSAR models that estimate physicochemical and ecotoxicological endpoints. ASTER will be available to international governmental agencies through the USEPA National Computing Center.

  6. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data.

    PubMed

    Zhu, Xiao; Kruhlak, Naomi L

    2014-07-01

    Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI.

  7. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data.

    PubMed

    Zhu, Xiao; Kruhlak, Naomi L

    2014-07-01

    Drug-induced liver injury (DILI) is one of the most common drug-induced adverse events (AEs) leading to life-threatening conditions such as acute liver failure. It has also been recognized as the single most common cause of safety-related post-market withdrawals or warnings. Efforts to develop new predictive methods to assess the likelihood of a drug being a hepatotoxicant have been challenging due to the complexity and idiosyncrasy of clinical manifestations of DILI. The FDA adverse event reporting system (AERS) contains post-market data that depict the morbidity of AEs. Here, we developed a scalable approach to construct a hepatotoxicity database using post-market data for the purpose of quantitative structure-activity relationship (QSAR) modeling. A set of 2029 unique and modelable drug entities with 13,555 drug-AE combinations was extracted from the AERS database using 37 hepatotoxicity-related query preferred terms (PTs). In order to determine the optimal classification scheme to partition positive from negative drugs, a manually-curated DILI calibration set composed of 105 negatives and 177 positives was developed based on the published literature. The final classification scheme combines hepatotoxicity-related PT data with supporting information that optimize the predictive performance across the calibration set. Data for other toxicological endpoints related to liver injury such as liver enzyme abnormalities, cholestasis, and bile duct disorders, were also extracted and classified. Collectively, these datasets can be used to generate a battery of QSAR models that assess a drug's potential to cause DILI. PMID:24721472

  8. Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling.

    PubMed

    Matthews, Edwin J; Kruhlak, Naomi L; Weaver, James L; Benz, R Daniel; Contrera, Joseph F

    2004-12-01

    The FDA's Spontaneous Reporting System (SRS) database contains over 1.5 million adverse drug reaction (ADR) reports for 8620 drugs/biologics that are listed for 1191 Coding Symbols for Thesaurus of Adverse Reaction (COSTAR) terms of adverse effects. We have linked the trade names of the drugs to 1861 generic names and retrieved molecular structures for each chemical to obtain a set of 1515 organic chemicals that are suitable for modeling with commercially available QSAR software packages. ADR report data for 631 of these compounds were extracted and pooled for the first five years that each drug was marketed. Patient exposure was estimated during this period using pharmaceutical shipping units obtained from IMS Health. Significant drug effects were identified using a Reporting Index (RI), where RI = (# ADR reports / # shipping units) x 1,000,000. MCASE/MC4PC software was used to identify the optimal conditions for defining a significant adverse effect finding. Results suggest that a significant effect in our database is characterized by > or = 4 ADR reports and > or = 20,000 shipping units during five years of marketing, and an RI > or = 4.0. Furthermore, for a test chemical to be evaluated as active it must contain a statistically significant molecular structural alert, called a decision alert, in two or more toxicologically related endpoints. We also report the use of a composite module, which pools observations from two or more toxicologically related COSTAR term endpoints to provide signal enhancement for detecting adverse effects. PMID:16472241

  9. Linkage between the Danish National Health Service Prescription Database, the Danish Fetal Medicine Database, and other Danish registries as a tool for the study of drug safety in pregnancy

    PubMed Central

    Pedersen, Lars H; Petersen, Olav B; Nørgaard, Mette; Ekelund, Charlotte; Pedersen, Lars; Tabor, Ann; Sørensen, Henrik T

    2016-01-01

    A linked population-based database is being created in Denmark for research on drug safety during pregnancy. It combines information from the Danish National Health Service Prescription Database (with information on all prescriptions reimbursed in Denmark since 2004), the Danish Fetal Medicine Database, the Danish National Registry of Patients, and the Medical Birth Registry. The new linked database will provide validated information on malformations diagnosed both prenatally and postnatally. The cohort from 2008 to 2014 will comprise 589,000 pregnancies with information on 424,000 pregnancies resulting in live-born children, ∼420,000 pregnancies undergoing prenatal ultrasound scans, 65,000 miscarriages, and 92,000 terminations. It will be updated yearly with information on ∼80,000 pregnancies. The cohort will enable identification of drug exposures associated with severe malformations, not only based on malformations diagnosed after birth but also including those having led to termination of pregnancy or miscarriage. Such combined data will provide a unique source of information for research on the safety of medications used during pregnancy. PMID:27274312

  10. Exploring barriers for health visitors' adaption of the Danish children's database.

    PubMed

    Pape-Haugaard, Louise; Haugaard, Karin; Carøe, Per; Høstgaard, Anna Marie

    2013-01-01

    Denmark has unique health informatics databases such as "The Children's Database" (CDB), which since 2009 has held data on all Danish children from birth until 17 years of age. In the current set-up a number of potential sources of error exist - both technical and human - which means that the data is flawed. The objective of this paper is both to clarify errors in the database and to enlighten the underlying mechanisms causing these errors. This is done through an ethnographic study using participant observations, interviews and workshops. Errors are documented through statistical analysis. The data show redundant records. This redundancy can be explained by multiple transmissions conducted by end users or systems, or a lack of validation methods in the National CDB. In our results three types of cases are presented: from health visitors at school, from health visitors visiting families and from health visitors at central health offices. PMID:23920857

  11. Danish Prostate Cancer Registry – methodology and early results from a novel national database

    PubMed Central

    Helgstrand, JT; Klemann, N; Røder, MA; Toft, BG; Brasso, K; Vainer, B; Iversen, P

    2016-01-01

    Background Systematized Nomenclature of Medicine (SNOMED) codes are computer-processable medical terms used to describe histopathological evaluations. SNOMED codes are not readily usable for analysis. We invented an algorithm that converts prostate SNOMED codes into an analyzable format. We present the methodology and early results from a new national Danish prostate database containing clinical data from all males who had evaluation of prostate tissue from 1995 to 2011. Materials and methods SNOMED codes were retrieved from the Danish Pathology Register. A total of 26,295 combinations of SNOMED codes were identified. A computer algorithm was developed to transcode SNOMED codes into an analyzable format including procedure (eg, biopsy, transurethral resection, etc), diagnosis, and date of diagnosis. For validation, ~55,000 pathological reports were manually reviewed. Prostate-specific antigen, vital status, causes of death, and tumor-node-metastasis classification were integrated from national registries. Results Of the 161,525 specimens from 113,801 males identified, 83,379 (51.6%) were sets of prostate biopsies, 56,118 (34.7%) were transurethral/transvesical resections of the prostate (TUR-Ps), and the remaining 22,028 (13.6%) specimens were derived from radical prostatectomies, bladder interventions, etc. A total of 48,078 (42.2%) males had histopathologically verified prostate cancer, and of these, 78.8% and 16.8% were diagnosed on prostate biopsies and TUR-Ps, respectively. Future perspectives A validated algorithm was successfully developed to convert complex prostate SNOMED codes into clinical useful data. A unique database, including males with both normal and cancerous histopathological data, was created to form the most comprehensive national prostate database to date. Potentially, our algorithm can be used for conversion of other SNOMED data and is available upon request. PMID:27729813

  12. Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases.

    PubMed

    Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare

    2007-01-01

    A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).

  13. QSAR Methods.

    PubMed

    Gini, Giuseppina

    2016-01-01

    In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals. PMID:27311459

  14. Preoperative airway assessment - experience gained from a multicentre cluster randomised trial and the Danish Anaesthesia Database.

    PubMed

    Nørskov, Anders Kehlet

    2016-05-01

    Difficulties with airway management in relation to general anaesthesia have been a challenge for the anaesthesiologist since the birth of anaesthesia. Massive landmark improvements have been made and general anaesthesia is now regarded as a safe procedure. However, rare, difficult airway management still occurs and it prompts increased risk of morbidity and mortality - especially when not anticipated. Several preoperative risk factors for airway difficulties have been identified, yet none have convincing diagnostic accuracy as stand alone tests. Combining several risk factors increase the predictive value of the test and multivariable risk models have been developed. The Simplified Airway Risk Index (SARI) is a predictive model developed for anticipation of a difficult direct laryngoscopy. However, neither the diagnostic accuracy of the SARI nor of any other model has been tested prospectively and compared with existing practice for airway assessment in a randomised trial setting. The first objective of this thesis was to quantify the proportion of unanticipated difficult intubation and difficult mask ventilation in Denmark. The second objective was to design a cluster randomised trial, using state of the art methodology, in order to test the clinical impact of using the SARI for preoperative airway assessment compared with a clinical judgement based on usual practice for airway assessment. Finally, to test if implementation of the SARI would reduce the proportion of unanticipated difficult intubation compared with usual care for airway assessment. This thesis is based on data from the Danish Anaesthesia Database (DAD). Paper 1 presents an observational cohort study on 188,064 patients who underwent tracheal intubation from 2008 to 2011. Data on the anaesthesiologists' preoperative anticipations of airway difficulties was compared with actual airway management conditions, thus enabling an estimation of the proportion of unanticipated difficulties with intubation

  15. How can the research potential of the clinical quality databases be maximized? The Danish experience.

    PubMed

    Nørgaard, M; Johnsen, S P

    2016-02-01

    In Denmark, the need for monitoring of clinical quality and patient safety with feedback to the clinical, administrative and political systems has resulted in the establishment of a network of more than 60 publicly financed nationwide clinical quality databases. Although primarily devoted to monitoring and improving quality of care, the potential of these databases as data sources in clinical research is increasingly being recognized. In this review, we describe these databases focusing on their use as data sources for clinical research, including their strengths and weaknesses as well as future concerns and opportunities. The research potential of the clinical quality databases is substantial but has so far only been explored to a limited extent. Efforts related to technical, legal and financial challenges are needed in order to take full advantage of this potential. PMID:26785952

  16. History and successes of QSAR in environmental applications

    SciTech Connect

    Veith, G.D.

    1994-12-31

    The history of the development of relationships between chemical structure and chemical behavior for assessing the safety of chemicals is marked by the struggle with timidity that so many areas of science face. Despite a continuous stream of successes for estimating properties and biological activity of chemicals from their structure, the field of QSAR has been met forcibly by the skeptics. In failing to articulate the potential savings in term of costs land test animals, QSAR researchers have enabled the skeptics to prevent a strategic QSAR program from being formed in either the private or the public sectors. QSAR must generate systematic, reference databases for the intrinsic properties of chemicals. Partitioning is such an intrinsic property. QSAR has succeeded not only in calculating hydrophobicity descriptors that control partitioning, but also in using these descriptors in counties relationships for specific endpoints. QSAR has also developed databases for potency. So many structure-toxicity relationships have been published that the potency of over 75 percent of all chemicals produced worldwide can be estimated without further animal testing. Biochemical persistence, as evidenced in biodegradability and/or tissue metabolism, lags behind due, in part, to a shortage of systematic databases. Several interesting approaches will be discussed. Finally, intrinsic reactivity and the selectivity of chemicals among competing interactions must be modeled. Since chemical reactivity holds the key to identifying genotoxic chemicals and other highly toxic chemicals, reactivity models are urgently needed. Recent QSAR advances for some forms of reactivity will be discussed.

  17. 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.

  18. 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

  19. QSAR Models at the US FDA/NCTR.

    PubMed

    Hong, Huixiao; Chen, Minjun; Ng, Hui Wen; Tong, Weida

    2016-01-01

    Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products. PMID:27311476

  20. 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

  1. Discrete Derivatives for Atom-Pairs as a Novel Graph-Theoretical Invariant for Generating New Molecular Descriptors: Orthogonality, Interpretation and QSARs/QSPRs on Benchmark Databases.

    PubMed

    Martínez-Santiago, Oscar; Millán-Cabrera, Reisel; Marrero-Ponce, Yovani; Barigye, Stephen J; Martínez-López, Yoan; Torrens, Francisco; Pérez-Giménez, Facundo

    2014-05-01

    indices and other 0-3D MDs is demonstrated by using principal component analysis on a dataset of 41 heterogeneous molecules. It is concluded that the graph derivative indices are independent indices containing important structural information to be used in QSPR/QSAR and drug design studies, and permit obtaining easier, more interpretable and robust mathematical models than the majority of those reported in the literature.

  2. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources.

    PubMed

    Chen, Baiyang; Zhang, Tian; Bond, Tom; Gan, Yiqun

    2015-12-15

    Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application.

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

    PubMed

    Tropsha, Alexander; Golbraikh, Alexander

    2007-01-01

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

  4. Dataset Modelability by QSAR

    PubMed Central

    Golbraikh, Alexander; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander

    2014-01-01

    We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (Correct Classification Rate above 0.7) for a binary dataset of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of the nearest neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 datasets and the threshold of 0.65 was found to separate non-modelable from the modelable datasets. PMID:24251851

  5. Robust Methods in Qsar

    NASA Astrophysics Data System (ADS)

    Walczak, Beata; Daszykowski, Michał; Stanimirova, Ivana

    A large progress in the development of robust methods as an efficient tool for processing of data contaminated with outlying objects has been made over the last years. Outliers in the QSAR studies are usually the result of an improper calculation of some molecular descriptors and/or experimental error in determining the property to be modelled. They influence greatly any least square model, and therefore the conclusions about the biological activity of a potential component based on such a model are misleading. With the use of robust approaches, one can solve this problem building a robust model describing the data majority well. On the other hand, the proper identification of outliers may pinpoint a new direction of a drug development. The outliers' assessment can exclusively be done with robust methods and these methods are to be described in this chapter

  6. A validation of the Danish microbiology database (MiBa) and incidence rate of Actinotignum schaalii (Actinobaculum schaalii) bacteraemia in Denmark.

    PubMed

    Bank, S; Søby, K M; Kristensen, L H; Voldstedlund, M; Prag, J

    2015-12-01

    Actinotignum schaalii (former named Actinobaculum schaalii) can cause urinary tract infections (UTIs) and bacteraemia, mainly in the elderly. A. schaalii is difficult to identify with conventional biochemical tests, and it is often overlooked if the urine is only cultured in ambient air. The aim of this study was to validate data from the nationwide Danish microbiology database (MiBa) with data from the laboratory information system (LIS) at the local department of microbiology in Viborg-Herning, and to evaluate the incidence rate of bacteraemia caused by A. schaalii in Denmark by using data from the MiBa. All departments of microbiology in Denmark report data to the MiBa. All microbiological samples with A. schaalii in Denmark were extracted for a period of 5 years from the MiBa and from the local LISs. All data obtained from our local LIS were also found in the MiBa, except for data on real-time PCR, which were not registered, owing to missing ID codes in the MiBa. From 2010 to 2014, there was a significant increase in the incidence rate of blood cultures with A. schaalii, from 1.8 to 6.8 cases per million, which was probably due to coincident implementation of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) in routine diagnostics. We found that A. schaalii caused bacteraemia and UTIs mainly in the elderly. In conclusion, the MiBa can be a useful source of nationwide microbiological data in Denmark. Our results suggest that the incidence rate of A. schaalii as a cause of bacteraemia has been underestimated, and that culture of urine in CO2 can improve the detection of A. schaalii.

  7. New public QSAR model for carcinogenicity

    PubMed Central

    2010-01-01

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

  8. 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.

  9. 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. PMID:17394240

  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. 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. 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

  13. 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

  14. 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...

  15. Combinatorial QSAR of ambergris fragrance compounds.

    PubMed

    Kovatcheva, Assia; Golbraikh, Alexander; Oloff, Scott; Xiao, Yun-De; Zheng, Weifan; Wolschann, Peter; Buchbauer, Gerhard; Tropsha, Alexander

    2004-01-01

    A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR

  16. 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.

  17. Robust fuzzy mappings for QSAR studies.

    PubMed

    Kumar, Mohit; Thurow, Kerstin; Stoll, Norbert; Stoll, Regina

    2007-05-01

    This study presents a new robust method of developing quantitative structure-activity relationship (QSAR) models based on fuzzy mappings. An important issue in QSAR modelling is of robustness, i.e., model should not undergo overtraining and model performance should be least sensitive to the modelling errors associated with the chosen descriptors and structure of the model. We establish robust input-output mappings for QSAR studies based on fuzzy "if-then" rules. The identification of these mappings (i.e. the construction of fuzzy rules) is based on a robust criterion that the maximum possible value of energy-gain from modelling errors to the identification errors is minimum. The robustness of proposed approach has been illustrated with simulation studies and QSAR modelling examples. The method of robust fuzzy mappings has been compared with Bayesian regularized neural networks through the QSAR modelling examples of (1) carboquinones' data set, (2) benzodiazepine data set, and (3) predicting the rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds.

  18. 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.

  19. Inferring multi-target QSAR models with taxonomy-based multi-task learning

    PubMed Central

    2013-01-01

    Background A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred between the QSAR models to improve the model accuracy. In this study, we present two different multi-task algorithms from the field of transfer learning that can exploit the similarity between several targets to transfer knowledge between the target specific QSAR models. Results We evaluated the two methods on simulated data and a data set of 112 human kinases assembled from the public database ChEMBL. The relatedness between the kinase targets was derived from the taxonomy of the humane kinome. The experiments show that multi-task learning increases the performance compared to training separate models on both types of data given a sufficient similarity between the tasks. On the kinase data, the best multi-task approach improved the mean squared error of the QSAR models of 58 kinase targets. Conclusions Multi-task learning is a valuable approach for inferring multi-target QSAR models for lead optimization. The application of multi-task learning is most beneficial if knowledge can be transferred from a similar task with a lot of in-domain knowledge to a task with little in-domain knowledge. Furthermore, the benefit increases with a decreasing overlap between the chemical space spanned by the tasks. PMID:23842210

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

    PubMed Central

    2014-01-01

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

  1. Toxico-Cheminformatics and QSPR Modeling of the Carcinogenic Potency Database

    EPA Science Inventory

    Report on the development of a tiered, confirmatory scheme for prediction of chemical carcinogenicity based on QSAR studies of compounds with available mutagenic and carcinogenic data. For 693 such compounds from the Carcinogenic Potency Database characterized molecular topologic...

  2. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

    PubMed

    Melo-Filho, Cleber C; Dantas, Rafael F; Braga, Rodolpho C; Neves, Bruno J; Senger, Mario R; Valente, Walter C G; Rezende-Neto, João M; Chaves, Willian T; Muratov, Eugene N; Paveley, Ross A; Furnham, Nicholas; Kamentsky, Lee; Carpenter, Anne E; Silva-Junior, Floriano P; Andrade, Carolina H

    2016-07-25

    Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents. PMID:27253773

  3. 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.

  4. 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...

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

    SciTech Connect

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

    2015-04-15

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

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

    PubMed

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

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database. PMID:26669717

  7. QSAR Models for Regulatory Purposes: Experiences and Perspectives

    NASA Astrophysics Data System (ADS)

    Benfenati, Emilio

    Quantitative structure-activity relationships (QSARs) are more and more discussed and used in several situations. Their application to legislative purposes stimulated a large debate in Europe on the recent legislation on industrial chemicals. To correctly assess the suitability of QSAR, the discussion has to be done depending on the target. Different targets modify the model evaluation and use. The application of QSAR for legislative purposes requires keeping into account the use of the values obtained through the QSAR models. False negatives should be minimized. The model should be robust, verified, and validated. Reproducibility and transparency are other important characteristics.

  8. Toward a general predictive QSAR model for gamma-secretase inhibitors.

    PubMed

    Ajmani, Subhash; Janardhan, Sridhara; Viswanadhan, Vellarkad N

    2013-08-01

    Gamma secretase (GS) is an appealing drug target for Alzheimer disease and cancer because of its central role in the processing of amyloid precursor protein and the notch family of proteins. In the absence of three-dimensional structure of GS, there is an urgent need for new methods for the prediction and screening of GS inhibitors, for facilitating discovery of novel GS inhibitors. The present study reports QSAR studies on diverse chemical classes comprising 233 compounds collected from the ChEMBL database. Herein, continuous [PLS regression and neural-network (NN)] and categorical QSAR models (NN and linear discriminant analysis) were developed to obtain pertinent descriptors responsible for variation of GS inhibitor potency. Also, SAR within various chemical classes of compounds is analyzed with respect to important QSAR descriptors, which revealed the significance of electronegative substitutions on aryl rings (PEOE3) in determining variation of GS inhibitor potency. Furthermore, substitution of acyclic amines with N-substituted cyclic amines appears to be favorable for enhancing GS inhibitor potency by increasing the values of sssN_Cnt and number of aliphatic rings. The models developed are statistically significant and improve our understanding of compounds contributing toward GS inhibitor potency and aid in the rational design of novel potent GS inhibitors. PMID:23612850

  9. 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

  10. Comparative QSAR studies on peptide deformylase inhibitors.

    PubMed

    Lee, Ji Young; Doddareddy, Munikumar Reddy; Cho, Yong Seo; Choo, Hyunah; Koh, Hun Yeong; Kang, Jae-Hoon; No, Kyoung Tai; Pae, Ae Nim

    2007-05-01

    Comparative quantitative structure-activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r (2) = 0.957, q (2) = 0.569; CoMSIA r (2) = 0.924, q (2) = 0.520; HQSAR r (2) = 0.860, q (2) = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r (2) values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor-ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. PMID:17333308

  11. (Q)SAR: A Tool for the Toxicologist.

    PubMed

    Steinbach, Thomas; Gad-McDonald, Samantha; Kruhlak, Naomi; Powley, Mark; Greene, Nigel

    2015-01-01

    A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them.

  12. Discovery of potent adenosine A2a antagonists as potential anti-Parkinson disease agents. Non-linear QSAR analyses integrated with pharmacophore modeling.

    PubMed

    Khanfar, Mohammad A; Al-Qtaishat, Saja; Habash, Maha; Taha, Mutasem O

    2016-07-25

    Adenosine A2A receptor antagonists are of great interest in the treatment for Parkinson's disease. In this study, we combined extensive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent Adenosine A2A antagonists. Genetic function algorithm (GFA) joined with k nearest neighbor (kNN) analyses were applied to build predictive QSAR models. Successful pharmacophores were complemented with exclusion spheres to improve their receiver operating characteristic curve (ROC) profiles. Best QSAR models and their associated pharmacophore hypotheses were validated by identification of several novel Adenosine A2A antagonist leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated IC50 value of 545.7 nM. PMID:27216633

  13. QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae.

    PubMed

    Gramatica, Paola; Cassani, Stefano; Roy, Partha Pratim; Kovarich, Simona; Yap, Chun Wei; Papa, Ester

    2012-12-01

    A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.

  14. 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.

  15. 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

  16. 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.

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

    PubMed Central

    2010-01-01

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

  18. Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening.

    PubMed

    Taha, Mutasem O; Bustanji, Yasser; Al-Bakri, Amal G; Yousef, Al-Motassem; Zalloum, Waleed A; Al-Masri, Ihab M; Atallah, Naji

    2007-03-01

    A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database. PMID:17035054

  19. Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening.

    PubMed

    Taha, Mutasem O; Bustanji, Yasser; Al-Bakri, Amal G; Yousef, Al-Motassem; Zalloum, Waleed A; Al-Masri, Ihab M; Atallah, Naji

    2007-03-01

    A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.

  20. 3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors

    PubMed Central

    Xie, Huiding; Qiu, Kaixiong; Xie, Xiaoguang

    2014-01-01

    Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q2 = 0.636, r2ncv = 0.988, r2pred = 0.658; CoMSIA: q2 = 0.843, r2ncv = 0.989, r2pred = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs. PMID:25405729

  1. The Odense University Pharmacoepidemiological Database (OPED)

    Cancer.gov

    The Odense University Pharmacoepidemiological Database is one of two large prescription registries in Denmark and covers a stable population that is representative of the Danish population as a whole.

  2. Quantum chemical variables in QSAR: A review

    SciTech Connect

    Hickey, J.P.; Passino-Reader, D.R.

    1994-12-31

    The integration of molecular orbital calculations with QSAR analyses has resulted in many ``theoretical`` indices. The authors will present a brief overview of these numerous quantum chemical parameters used in QSAR, as well as a small sampling of their applications (e.g., correlation of carcinogenicity with structure for PAHS, and MO calculations to predict toxicity of PCDDs and PCDFS). The quantum mechanical indices described here are compiled from many sources and are generally obtained from classical molecular mechanical methods, as well as from calculated molecular wavefunctions. The quality of the model or wave function and, consequently, of the indices depend entirely on the formalism and the level of approximation used. Because the molecular waveform is often described as a linear combination of atomic orbitals (LCAO), one can easily obtain some of the indices. These include the atomic charges, sigma and pi charges, frontier electron densities, E{sub HOMO}, and E{sub LUMA} and the superdelocalizability parameters. Parameter computation methods and appropriate software (and sources) will be highlighted.

  3. Tuning HERG out: antitarget QSAR models for drug development.

    PubMed

    Braga, Rodolpho C; Alves, Vinicius M; Silva, Meryck F B; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H

    2014-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure-activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

  4. Using Toxicological Evidence from QSAR Models in Practice

    EPA Science Inventory

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

  5. A novel algorithm for QSAR (quantitative structure-activity relationships)

    SciTech Connect

    Carter, S. ); Nikolic, S.; Trinajstic, N. )

    1989-01-01

    A novel approach to quantitative structure-activity relationships (QSAR) is proposed. It is based on the molecular descriptor named the stereo-identification (SID) number. The applicability of this approach to QSAR studies is tested on aquatic toxicities of phenols against fathead minnows (Phimephales promelas). Our approach reproduced successfully the bioactivities of phenols and is superior to the Hall-Kier model based on Randic's connectivity index.

  6. Quantitative Structure-Activity Relationships (QSARs) - Applications and Methodology

    NASA Astrophysics Data System (ADS)

    Cronin, Mark T. D.

    The aim of this introduction is to describe briefly the applications and methodologies involved in (Q)SAR and relate these to the various chapters in this volume. This chapter gives the reader an overview of how, why and where in silico methods, including (Q)SAR, have been utilized to predict endpoints as diverse as those from pharmacology and toxicology. It provides an illustration of how all the various topics in this book interweave to form a single coherent area of science.

  7. QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ).

    PubMed

    Davis, G Dicky John; Vasanthi, A Hannah Rachel

    2015-08-30

    Marine algae are prolific source of bioactive secondary metabolites and are found to be active against different cancer cell lines. QSAR studies will explicate the significance of a particular class of descriptor in eliciting anticancer activity against a cancer type. Marine algal compounds showing anticancer activity against six different cancer cell lines namely MCF-7, A431, HeLa, HT-29, P388 and A549 taken from Seaweed metabolite database were subjected to comprehensive QSAR modeling studies. A hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction and multiple linear regression analysis (MLR) approach was used as fitness functions. Cell lines HeLa and MCF-7 showed good statistical quality (R(2)∼0.75, Q(2)∼0.65) followed by A431, HT29 and P388 cell lines with reasonable statistical values (R(2)∼0.70, Q(2)∼0.60). The models developed were interpretable, with good statistical and predictive significance. Molecular descriptor analyses revealed that Baumann's alignment-independent topological descriptors had a major role in variation of activity along with other descriptors. Incidentally, earlier QSAR analysis on a variety of chemically diverse PKBα inhibitors revealed Baumann's alignment-independent topological descriptors that differentiated the molecules binding to Protein kinase B (PKBα) kinase or PH domain, hence a docking study of two crystal structures of PKBβ was performed for identification of novel ATP-competitive inhibitors of PKBβ. Five compounds had a good docking score and Callophycin A showed better ligand efficiency than other PKBβ inhibitors. Furthermore in silico pharmacokinetic and toxicity studies also showed that Callophycin A had a high drug score (0.85) compared to the other inhibitors. These results encourages discovering novel inhibitors for cancer therapeutic targets by screening metabolites from marine algae.

  8. 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

  9. Antioxidant QSAR modeling as exemplified on polyphenols.

    PubMed

    Lucić, Bono; Amić, Dragan; Trinajstić, Nenad

    2008-01-01

    Methodology for deriving quantitative structure-activity relationship (QSAR) models based on computed molecular descriptors, representing numerically structural features of polyphenols, and applicable to the antioxidant activity of polyphenols is delineated. The application of this methodology is illustrated on a data set of 100 polyphenols. Prior to the computation of molecular descriptors, molecular structures are coded in the SMILES form, a computer-acceptable version of structure, and then converted to the 3D form by the CORINA program. Using 3D structures, molecular descriptors can be calculated by one of several programs developed (we used the DRAGON program in this study). Finally, using computer program for selection of most important descriptors in the model, a two-descriptor model is selected and its use is illustrated.

  10. In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening.

    PubMed

    Chaudhari, Prashant; Bari, Sanjay

    2016-02-01

    c-KIT is a component of the platelet-derived growth factor receptor family, classified as type-III receptor tyrosine kinase. c-KIT has been reported to be involved in, small cell lung cancer, other malignant human cancers, and inflammatory and autoimmune diseases associated with mast cells. Available c-KIT inhibitors suffer from tribulations of growing resistance or cardiac toxicity. A combined in silico pharmacophore and structure-based virtual screening was performed to identify novel potential c-KIT inhibitors. In the present study, five molecules from the ZINC database were retrieved as new potential c-KIT inhibitors, using Schrödinger's Maestro 9.0 molecular modeling suite. An atom-featured 3D QSAR model was built using previously reported c-KIT inhibitors containing the indolin-2-one scaffold. The developed 3D QSAR model ADHRR.24 was found to be significant (R2 = 0.9378, Q2 = 0.7832) and instituted to be sufficiently robust with good predictive accuracy, as confirmed through external validation approaches, Y-randomization and GH approach [GH score 0.84 and Enrichment factor (E) 4.964]. The present QSAR model was further validated for the OECD principle 3, in that the applicability domain was calculated using a "standardization approach." Molecular docking of the QSAR dataset molecules and final ZINC hits were performed on the c-KIT receptor (PDB ID: 3G0E). Docking interactions were in agreement with the developed 3D QSAR model. Model ADHRR.24 was explored for ligand-based virtual screening followed by in silico ADME prediction studies. Five molecules from the ZINC database were obtained as potential c-KIT inhibitors with high in -silico predicted activity and strong key binding interactions with the c-KIT receptor.

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

    PubMed Central

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

    2003-01-01

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

  12. 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

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

    PubMed

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

    2015-06-11

    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.

  15. Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR.

    PubMed

    Cortes-Ciriano, Isidro

    2016-08-22

    Compound physicochemical properties favoring in vitro potency are not always correlated to desirable pharmacokinetic profiles. Therefore, using potency (i.e., IC50) as the main criterion to prioritize candidate drugs at early stage drug discovery campaigns has been questioned. Yet, the vast majority of the virtual screening models reported in the medicinal chemistry literature predict the biological activity of compounds by regressing in vitro potency on topological or physicochemical descriptors. Two studies published in this journal showed that higher predictive power on external molecules can be achieved by using ligand efficiency indices as the dependent variable instead of a metric of potency (IC50) or binding affinity (Ki). The present study aims at filling the shortage of a thorough assessment of the predictive power of ligand efficiency indices in QSAR. To this aim, the predictive power of 11 ligand efficiency indices has been benchmarked across four algorithms (Gradient Boosting Machines, Partial Least Squares, Random Forest, and Support Vector Machines), two descriptor types (Morgan fingerprints, and physicochemical descriptors), and 29 data sets collected from the literature and ChEMBL database. Ligand efficiency metrics led to the highest predictive power on external molecules irrespective of the descriptor type or algorithm used, with an R(2)test difference of ∼0.3 units and a this difference ∼0.4 units when modeling small data sets and a normalized RMSE decrease of >0.1 units in some cases. Polarity indices, such as SEI and NSEI, led to higher predictive power than metrics based on molecular size, i.e., BEI, NBEI, and LE. LELP, which comprises a polarity factor (cLogP) and a size parameter (LE) constantly led to the most predictive models, suggesting that these two properties convey a complementary predictive signal. Overall, this study suggests that using ligand efficiency indices as the dependent variable might be an efficient strategy to model

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

    PubMed

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

    2014-03-01

    In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six. PMID:24801104

  17. Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase.

    PubMed

    Veselinović, J B; Nikolić, G M; Trutić, N V; Živković, J V; Veselinović, A M

    2015-06-01

    A series of 278 organophosphate compounds acting as acetylcholinesterase inhibitors has been studied. The Monte Carlo method was used as a tool for building up one-variable quantitative structure-activity relationship (QSAR) models for acetylcholinesterase inhibition activity based on the principle that the target endpoint is treated as a random event. As an activity, bimolecular rate constants were used. The QSAR models were based on optimal descriptors obtained from Simplified Molecular Input-Line Entry System (SMILES) used for the representation of molecular structure. Two modelling approaches were examined: (1) 'classic' training-test system where the QSAR model was built with one random split into a training, test and validation set; and (2) the correlation balance based QSAR models were built with two random splits into a sub-training, calibration, test and validation set. The DModX method was used for defining the applicability domain. The obtained results suggest that studied activity can be determined with the application of QSAR models calculated with the Monte Carlo method since the statistical quality of all build models was very good. Finally, structural indicators for the increase and the decrease of the bimolecular rate constant are defined. The possibility of using these results for the computer-aided design of new organophosphate compounds is presented.

  18. 4D-fingerprints, universal QSAR and QSPR descriptors.

    PubMed

    Senese, Craig L; Duca, J; Pan, D; Hopfinger, A J; Tseng, Y J

    2004-01-01

    An elusive goal in the field of chemoinformatics and molecular modeling has been the generation of a set of descriptors that, once calculated for a molecule, may be used in a wide variety of applications. Since such universal descriptors are generated free from external constraints, they are inherently independent of the data set in which they are employed. The realization of a set of universal descriptors would significantly streamline such chemoinformatics tasks as virtual high-throughout screening (VHTS) and toxicity profiling. The current study reports the derivation and validation of a potential set of universal descriptors, referred to as the 4D-fingerprints. The 4D-fingerprints are derived from the 4D-molecular similarity analysis. To evaluate the applicability of the 4D-fingerprints as universal descriptors, they are used to generate descriptive QSAR models for 5 independent training sets. Each of the training sets has been analyzed previously by several varying QSAR methods, and the results of the models generated using the 4D-fingerprints are compared to the results of the previous QSAR analyses. It was found that the models generated using the 4D-fingerprints are comparable in quality, based on statistical measures of fit and test set prediction, to the previously reported models for the other QSAR methods. This finding is particularly significant considering the 4D-fingerprints are generated independent of external constraints such as alignment, while the QSAR methods used for comparison all require an alignment analysis.

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

    PubMed Central

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

    2015-01-01

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

  20. On the development and validation of QSAR models.

    PubMed

    Gramatica, Paola

    2013-01-01

    The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.

  1. 3D-QSAR - Applications, Recent Advances, and Limitations

    NASA Astrophysics Data System (ADS)

    Sippl, Wolfgang

    Three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques are the most prominent computational means to support chemistry within drug design projects where no three-dimensional structure of the macromolecular target is available. The primary aim of these techniques is to establish a correlation of biological activities of a series of structurally and biologically characterized compounds with the spatial fingerprints of numerous field properties of each molecule, such as steric demand, lipophilicity, and electrostatic interactions. The number of 3D-QSAR studies has exponentially increased over the last decade, since a variety of methods are commercially available in user-friendly, graphically guided software. In this chapter, we will review recent advances, known limitations, and the application of receptor-based 3D-QSAR

  2. Prediction of Intracellular Localization of Fluorescent Dyes Using QSAR Models.

    PubMed

    Uchinomiya, Shohei; Horobin, Richard W; Alvarado-Martínez, Enrique; Peña-Cabrera, Eduardo; Chang, Young-Tae

    2016-01-01

    Control of fluorescent dye localization in live cells is crucial for fluorescence imaging. Here, we describe quantitative structure activity relation (QSAR) models for predicting intracellular localization of fluorescent dyes. For generating the QSAR models, electric charge (Z) calculated by pKa, conjugated bond number (CBN), the largest conjugated fragment (LCF), molecular weight (MW) and log P were used as parameters. We identified the intracellular localization of 119 BODIPY dyes in live NIH3T3 cells, and assessed the accuracy of our models by comparing their predictions with the observed dye localizations. As predicted by the models, no BODIPY dyes localized in nuclei or plasma membranes. The accuracy of the model for localization in fat droplets was 92%, with the models for cytosol and lysosomes showing poorer agreement with observed dye localization, albeit well above chance levels. Overall therefore the utility of QSAR models for predicting dye localization in live cells was clearly demonstrated. PMID:27055752

  3. Variable selection for QSAR by artificial ant colony systems.

    PubMed

    Izrailev, S; Agrafiotis, D K

    2002-01-01

    Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of the input variables that results in the most predictive model. In this paper we describe an optimization technique for variable selection based on artificial ant colony systems. The algorithm is inspired by the behavior of real ants, which are able to find the shortest path between a food source and their nest using deposits of pheromone as a communication agent. The underlying basic self-organizing principle is exploited for the construction of parsimonious QSAR models based on neural networks for several classical QSAR data sets. PMID:12184383

  4. 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

  5. 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.

  6. 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. PMID:24351051

  7. New QSAR models for polyhalogenated aromatics

    SciTech Connect

    Nevalainen, T.; Kolehmainen, E. . Dept. of Chemistry)

    1994-10-01

    Electronic properties of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), polychlorinated biphenyls (PCBs), and polychlorinated diphenyl ethers (PCDEs) were calculated using the semi-empirical AM 1 method. The calculated electronic descriptions--the energy of the lowest unoccupied molecular orbital (E[sub LUMO]), the energy of the highest occupied molecular orbital (E[sub HOMO]), the E[sub LUMO]-E[sub HOMO] gap (dE), and molecular polarizability--are related to the Ah receptor binding affinity values of PCDDs, PCDFs, and PCBs and immunotoxicity values for PCDEs. The quantitative structure-activity relationships (QSARs) based on chlorine substitution patterns were also constructed, and they proved to be very predictive for Ah receptor binding. Significant correlations of the electronic factors were found between the dE and Ah receptor binding affinities for PCDDs, PCDFs, and PCBs and for immunotoxicity of PCDEs. A combination of descriptors E[sub LUMO] and the total number of chlorine atoms attached to a molecule (n[sub Cl]) gives significant correlation for the Ah receptor binding of PCDFs and for immunotoxicity of PCDEs. Hydrophobicity values taken from the literature were shown to be non-significant for toxicity prediction of these polychlorinated compounds.

  8. Development and implementation of (Q)SAR modeling within the CHARMMing Web-user interface

    PubMed Central

    Weidlich, Iwona E.; Pevzner, Yuri; Miller, Benjamin T.; Filippov, Igor V.; Woodcock, H. Lee; Brooks, Bernard R.

    2014-01-01

    Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a Web-based tool for SAR and QSAR modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms – Random Forest, Support Vector Machine (SVM), Stochastic Gradient Descent, Gradient Tree Boosting etc. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. PMID:25362883

  9. QSAR in predictive models for ecological risk assessment

    SciTech Connect

    Passino-Reader, D.R.; Hickey, J.P.

    1994-12-31

    The end use of toxicity and exposure data is risk assessment to determine the probability that receptors experience harmful effects from exposure to environmental contaminants at a site. Determination of processes and development of predictive models precede the collection of data for risk assessment. The presence of hundreds of contaminants at a site and absence of data for many contaminants lead to the use of QSAR to implement the models. Examples of the use of linear salvation energy relationships (LSER) to provide estimates of aquatic toxicity and exposure endpoints will be provided. Integration of QSAR estimates and measured data must be addressed in the uncertainty analysis accompanying ecological risk assessment.

  10. 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

    , comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.

  11. Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

    PubMed

    Nicolotti, Orazio; Altomare, Cosimo; Pellegrini-Calace, Marialuisa; Carotti, Angelo

    2004-01-01

    Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was

  12. Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

    PubMed

    Nicolotti, Orazio; Altomare, Cosimo; Pellegrini-Calace, Marialuisa; Carotti, Angelo

    2004-01-01

    Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was

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

    EPA Science Inventory

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

  14. SAR/QSAR methods in public health practice

    SciTech Connect

    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.

  15. 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...

  16. Fragment-based QSAR: perspectives in drug design.

    PubMed

    Salum, Lívia B; Andricopulo, Adriano D

    2009-08-01

    Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

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

    PubMed Central

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

    2015-01-01

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

  18. 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.

  19. Danish auroral science history

    NASA Astrophysics Data System (ADS)

    Stauning, P.

    2011-01-01

    Danish auroral science history begins with the early auroral observations made by the Danish astronomer Tycho Brahe during the years from 1582 to 1601 preceding the Maunder minimum in solar activity. Included are also the brilliant observations made by another astronomer, Ole Rømer, from Copenhagen in 1707, as well as the early auroral observations made from Greenland by missionaries during the 18th and 19th centuries. The relations between auroras and geomagnetic variations were analysed by H. C. Ørsted, who also played a vital role in the development of Danish meteorology that came to include comprehensive auroral observations from Denmark, Iceland and Greenland as well as auroral and geomagnetic research. The very important auroral investigations made by Sophus Tromholt are outlined. His analysis from 1880 of auroral observations from Greenland prepared for the significant contributions from the Danish Meteorological Institute, DMI, (founded in 1872) to the first International Polar Year 1882/83, where an expedition headed by Adam Paulsen was sent to Greenland to conduct auroral and geomagnetic observations. Paulsen's analyses of the collected data gave many important results but also raised many new questions that gave rise to auroral expeditions to Iceland in 1899 to 1900 and to Finland in 1900 to 1901. Among the results from these expeditions were 26 unique paintings of the auroras made by the artist painter, Harald Moltke. The expedition to Finland was headed by Dan la Cour, who later as director of the DMI came to be in charge of the comprehensive international geomagnetic and auroral observations made during the Second International Polar Year in 1932/33. Finally, the article describes the important investigations made by Knud Lassen during, among others, the International Geophysical Year 1957/58 and during the International Quiet Sun Year (IQSY) in 1964/65. With his leadership the auroral and geomagnetic research at DMI reached a high international

  20. 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

  1. 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

  2. External validation of a QSAR for the acute toxicity of halogenated aliphatic hydrocarbons

    SciTech Connect

    Eriksson, L.; Jonsson, J. . Dept. of Organic Chemistry); Berglind, R. . NBC-Defense Research)

    1993-07-01

    The validation of the predictive capability of a quantitative structure-activity relationship (QSAR) is a significant step toward the construction of a reliable model. This point is discussed and illustrated with data for a class of halogenated aliphatic hydrocarbons. For this class of compounds, a QSAR concerning their acute toxicity toward rate was recently published. This QSAR is verified in this by selecting and testing an external validation set comprising six compounds. The QSAR is also used for predicting the acute toxicity of 28 nontested members of this class.

  3. 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

  4. 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

  5. The Danish National Lymphoma Registry: Coverage and Data Quality

    PubMed Central

    Arboe, Bente; El-Galaly, Tarec Christoffer; Clausen, Michael Roost; Munksgaard, Peter Svenssen; Stoltenberg, Danny; Nygaard, Mette Kathrine; Klausen, Tobias Wirenfeldt; Christensen, Jacob Haaber; Gørløv, Jette Sønderskov; Brown, Peter de Nully

    2016-01-01

    Background The Danish National Lymphoma Register (LYFO) prospectively includes information on all lymphoma patients newly diagnosed at hematology departments in Denmark. The validity of the clinical information in the LYFO has never been systematically assessed. Aim To test the coverage and data quality of the LYFO. Methods The coverage was tested by merging data of the LYFO with the Danish Cancer Register and the Danish National Patient Register, respectively. The validity of the LYFO was assessed by crosschecking with information from medical records in subgroups of patients. A random sample of 3% (N = 364) was made from all patients in the LYFO. In addition, four subtypes of lymphomas were validated: CNS lymphomas, diffuse large B-cell lymphomas, peripheral T-cell lymphomas, and Hodgkin lymphomas. A total of 1,706 patients from the period 2000–2012 were included. The positive predictive values (PPVs) and completeness of selected variables were calculated for each subgroup and for the entire cohort of patients. Results The comparison of data from the LYFO with the Danish Cancer Register and the Danish National Patient Register revealed a high coverage. In addition, the data quality was good with high PPVs (87% to 100%), and high completeness (92% to 100%). Conclusion The LYFO is a unique, nationwide clinical database characterized by high validity, good coverage and prospective data entry. It represents a valuable resource for future lymphoma research. PMID:27336800

  6. Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities.

    PubMed

    Frid, Anna A; Matthews, Edwin J

    2010-04-01

    This report describes the use of three quantitative structure-activity relationship (QSAR) programs to predict drug-related cardiac adverse effects (AEs), BioEpisteme, MC4PC, and Leadscope Predictive Data Miner. QSAR models were constructed for 9 cardiac AE clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes) and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). The models were based on a database of post-marketing AEs linked to 1632 chemical structures, and identical training data sets were configured for three QSAR programs. Model performance was optimized and shown to be affected by the ratio of the number of active to inactive drugs. Results revealed that the three programs were complementary and predictive performances using any single positive, consensus two positives, or consensus three positives were as follows, respectively: 70.7%, 91.7%, and 98.0% specificity; 74.7%, 47.2%, and 21.0% sensitivity; and 138.2, 206.3, and 144.2 chi(2). In addition, a prospective study using AE data from the U.S. Food and Drug Administration's (FDA's) MedWatch Program showed 82.4% specificity and 94.3% sensitivity. Furthermore, an external validation study of 18 drugs with serious cardiotoxicity not considered in the models had 88.9% sensitivity. PMID:19941924

  7. A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology.

    PubMed

    Gramatica, Paola; Sangion, Alessandro

    2016-06-27

    In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models.

  8. A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology.

    PubMed

    Gramatica, Paola; Sangion, Alessandro

    2016-06-27

    In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models. PMID:27218604

  9. A general method for exploiting QSAR models in lead optimization.

    PubMed

    Lewis, Richard A

    2005-03-10

    Computer-aided drug design tools can generate many useful and powerful models that explain structure-activity relationship (SAR) observations in a quantitative manner. These models can use many different descriptors, functional forms, and methods from simple linear equations through to multilayer neural nets. Using a model, a medicinal chemist can compute an activity, given a structure, but it is much harder to work out what changes are needed to make a structure more active. The impact of a model on the design process would be greatly enhanced if the model were more interpretable to the bench chemist. This paper describes a new protocol for performing automated iterative quantitative structure-activity relationship (QSAR) studies and presents the results of experiments on two QSAR sets from the literature. The fundamental goal of this work is to try to assist the chemist in his search for what to make next.

  10. Predicting stability constants of various chelating agents using QSAR technology

    SciTech Connect

    Okey, R.W.; Lin, S.W.; Hong, P.K.A.

    1995-12-31

    The practice of capturing metals from contaminated soil slurry often involves the use of organics as chelators. This work was undertaken to develop information on the molecular characteristics which optimize the removal or the complexation of cadmium, copper, lead and zinc. Quantitative structure-activity relationship (QSAR) technology was employed using special techniques developed for the determination of the correct set of variables. The linear free energy relationship was applied using a 183 case data set to obtain regression coefficients. Equations obtained are provided. The differences in the coefficients and variables may be used as a guide in selecting the optimum chelator for a specific metal. The use of QSAR technology appears effective in furthering the understanding of metal-chelator relationships. A variable set combining molecular connectivity indices and fragments or groups can be used to minimize the size of the data set required for a valid regression and for the avoidance of collinearity problems.

  11. Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

    PubMed

    Maganti, Lakshmi; Das, Sanjit Kumar; Mascarenhas, Nahren Manuel; Ghoshal, Nanda

    2011-10-01

    The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified. PMID:27468106

  12. 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.

  13. 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. PMID:26748251

  14. Molecular quantum similarity and the fundamentals of QSAR.

    PubMed

    Besalú, Emili; Gironés, Xavier; Amat, Lluís; Carbó-Dorca, Ramon

    2002-05-01

    A general overview on quantum similarity and applications to QSAR is presented. The concepts regarding quantum similarity from its theoretical foundation and consecutive development, involving mathematical formulation and similarity measures, are presented and complemented with application examples. The practical part, based on the well-known Crammer 31 steroids set, covers approximate quantum similarity calculations, molecular superposition, and statistics. In this way, the reader will find both basic general information and applicability of quantum similarity. PMID:12020166

  15. 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.

  16. Mechanism-Based QSAR Modeling of Skin Sensitization.

    PubMed

    Dearden, J C; Hewitt, M; Roberts, D W; Enoch, S J; Rowe, P H; Przybylak, K R; Vaughan-Williams, G D; Smith, M L; Pillai, G G; Katritzky, A R

    2015-10-19

    Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data sets of skin sensitizers, we have allocated each sensitizing chemical to one of 10 mechanistic categories and then developed good QSAR models for the seven categories that have a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity.

  17. QSAR modeling of the inhibition of glycogen synthase kinase-3.

    PubMed

    Katritzky, Alan R; Pacureanu, Liliana M; Dobchev, Dimitar A; Fara, Dan C; Duchowicz, Pablo R; Karelson, Mati

    2006-07-15

    Quantitative structure-activity relationship (QSAR) models of the biological activity (pIC50) of 277 inhibitors of Glycogen Synthase Kinase-3 (GSK-3) are developed using geometrical, topological, quantum mechanical, and electronic descriptors calculated by CODESSA PRO. The linear (multilinear regression) and nonlinear (artificial neural network) models obtained link the structures to their reported activity pIC50. The results are discussed in the light of the main factors that influence the inhibitory activity of the GSK-3 enzyme.

  18. (Q)SAR modeling and safety assessment in regulatory review.

    PubMed

    Kruhlak, N L; Benz, R D; Zhou, H; Colatsky, T J

    2012-03-01

    The ability to predict clinical safety based on chemical structures is becoming an increasingly important part of regulatory decision making. (Quantitative) structure-activity relationship ((Q)SAR) models are currently used to evaluate late-arising safety concerns and possible nonclinical effects of a drug and its related compounds when adequate safety data are absent or equivocal. Regulatory use will likely increase with the standardization of analytical approaches, more complete and reliable data collection methods, and a better understanding of toxicity mechanisms.

  19. 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.

  20. A group center overlap based approach for "3D QSAR" studies on TIBO derivatives.

    PubMed

    Sapre, Nitin S; Gupta, Swagata; Pancholi, Nilanjana; Sapre, Neelima

    2009-04-30

    Current challenges in drug designing and lead optimization has reached a bottle neck where the main onus lies on rigorous validation to afford robust and predictive models. In the present study, we have suggested that predictive structure-activity relationship (SAR) models based on robust statistical analyses can serve as effective screening tools for large volume of compounds present either in chemical databases or in virtual libraries. 3D descriptors derived from the similarity-based alignment of molecules with respect to group center overlap from each individual template point and other "alignment averaged," but significant descriptors (ClogP, molar refractivity, connolly accessible area) were used to generate QSAR models. The results indicated that the artificial neural network method (r(2) = 0.902) proved to be superior to the multiple linear regression method (r(2) = 0.810). Cross validation of the models with an external set was reasonably satisfactory. Screening PubChem compound database based on the models obtained, yielded 14 newer modified compounds belonging to the TIBO class of inhibitors, as well as, two novel scaffolds, with enhanced binding efficacy as hits. These hits may be targeted toward potent lead-optimization and help in designing and synthesizing new compounds with potential therapeutic utility. PMID:18785154

  1. 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.

  2. 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. PMID:25660478

  3. QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors.

    PubMed

    Speck-Planche, Alejandro; Kleandrova, Valeria V; Rojas-Vargas, Julio A

    2011-11-01

    The increasing resistance of several phytopathogenic fungal species to the existing agrochemical fungicides has alarmed to the worldwide scientific community. There is no available methodology to predict in an efficient way if a new fungicide will have resistance risk due to fungal species which cause considerable crop losses. In an attempt to overcome this problem, a multi-resistance risk QSAR model, based on substructural descriptors was developed from a heterogeneous database of compounds. The purpose of this model is the classification, design, and prediction of agrochemical fungicides according to resistance risk categories. The QSAR model classified correctly 85.11% of the fungicides and the 85.07% of the inactive compounds in the training series, for an accuracy of 85.08%. In the prediction series, the percentages of correct classification were 85.71 and 86.55% for fungicides and inactive compounds, respectively, with an accuracy of 86.39%. Some fragments were extracted and their quantitative contributions to the fungicidal activity were calculated taking into consideration the different resistance risk categories for agrochemical fungicides. In the same way, some fragments present in molecules with fungicidal activity and with negative contributions were analyzed like structural alerts responsible of resistance risk.

  4. 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

  5. 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.

  6. ITERATIVE PROCESS OF QSAR BUILDING AND STRATEGIC TESTING: PREDICTING ER BINDING AFFINITY

    EPA Science Inventory

    Basic principles of QSAR model development and application are discussed. The most difficult step in QSAR application for regulatory use may be determining when a model is sufficiently improved to provide predictions for a specified chemical domain of regulatory concern. The iter...

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

    EPA Science Inventory

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-09-01

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

  9. Treating chemical diversity in QSAR analysis: modeling diverse HIV-1 integrase inhibitors using 4D fingerprints.

    PubMed

    Iyer, Manisha; Hopfinger, A J

    2007-01-01

    A set of 213 compounds across 12 structurally diverse classes of HIV-1 integrase inhibitors was used to develop and evaluate a combined clustering and QSAR modeling methodology to construct significant, reliable, and robust models for structurally diverse data sets. The trial-descriptor pool for both clustering- and QSAR-model building consisted of 4D fingerprints and classic QSAR descriptors. Clustering was carried out using a combination of the partitioning around medoids method and divisive hierarchical clustering. QSAR models were constructed for members of each cluster by linear-regression fitting and model optimization using the genetic function approximation. The 12 structurally diverse classes of integrase inhbitors were partitioned into five clusters from which corresponding QSAR models, overwhelmingly composed of 4D fingerprint descriptors, were constructed. Analysis of the five QSAR models suggests that three models correspond to structurally diverse inhibitors that likely bind at a common site on integrase characterized by a common inhibitor hydrogen-bond donor, but involving somewhat different alignments and/or poses for the inhibitors of each of the three clusters. The particular alignments for the inhibitors of each of the three QSAR models involve specific distributions of nonpolar groups over the inhibitors. The two other clusters, one for coumarins and the other for depsides and depsidones, lead to QSAR models with less-defined pharmacophores, likely representing an inhibitor binding to a site(s) different from that of the other nine classes of inhibitors. Overall, the clustering and QSAR methodology employed in this study suggests that it can meaningfully partition structurally diverse compounds expressing a common endpoint in such a manner that leads to statistically significant and pharmacologically insightful composite QSAR models. PMID:17661457

  10. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

    SciTech Connect

    Valencia, Antoni; Prous, Josep; Mora, Oscar; Sadrieh, Nakissa; Valerio, Luis G.

    2013-12-15

    As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation tests

  11. Synthesis, antimicrobial evaluation and QSAR studies of stearic acid derivatives.

    PubMed

    Tahlan, S; Kumar, P; Narasimhan, B

    2014-02-01

    A series of Schiff bases (1-17) and esters (18-28) of stearic acid was synthesized and characterized by physicochemical as well as spectral means. The synthesized compounds were evaluated in vitro for their antimicrobial activity by tube dilution method. The antimicrobial screening results indicated that the compounds having electron releasing groups on benzylidene nucleus were found to be more active against bacterial strains and compounds having electron withdrawing groups on benzylidene nucleus were found to be more active against fungal strains. QSAR studies demonstrated that electronic parameters dipole moment (µ) and total energy (Te) were the most important descriptors in describing the antimicrobial activity of synthesized stearic acid derivatives.

  12. 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

  13. 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.

  14. QSAR modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection.

    PubMed

    Ghosh, P; Bagchi, M C

    2009-01-01

    With a view to the rational design of selective quinoxaline derivatives, 2D and 3D-QSAR models have been developed for the prediction of anti-tubercular activities. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the chemico-biological interaction. Genetic algorithm (GA) and simulated annealing (SA) are applied as variable selection methods for model development. 2D-QSAR modeling using GA or SA based partial least squares (GA-PLS and SA-PLS) methods identified some important topological and electrostatic descriptors as important factor for tubercular activity. Kohonen network and counter propagation artificial neural network (CP-ANN) considering GA and SA based feature selection methods have been applied for such QSAR modeling of Quinoxaline compounds. Out of a variable pool of 380 molecular descriptors, predictive QSAR models are developed for the training set and validated on the test set compounds and a comparative study of the relative effectiveness of linear and non-linear approaches has been investigated. Further analysis using 3D-QSAR technique identifies two models obtained by GA-PLS and SA-PLS methods leading to anti-tubercular activity prediction. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The results indicate that SA is a very effective variable selection approach for such 3D-QSAR modeling.

  15. QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs.

    PubMed

    Li, J; Gramatica, P

    2010-10-01

    Endocrine disrupting chemicals (EDCs) are suspected of posing serious threats to human and wildlife health through a variety of mechanisms, these being mainly receptor-mediated modes of action. It is reported that some EDCs exhibit dual activities as estrogen receptor (ER) and androgen receptor (AR) binders. Indeed, such compounds can affect the normal endocrine system through a dual complex mechanism, so steps should be taken not only to identify them a priori from their chemical structure, but also to prioritize them for experimental tests in order to reduce and even forbid their usage. To date, very few EDCs with dual activities have been identified. The present research uses QSARs, to investigate what, so far, is the largest and most heterogeneous ER binder data set (combined METI and EDKB databases). New predictive classification models were derived using different modelling methods and a consensus approach, and these were used to virtually screen a large AR binder data set after strict validation. As a result, 46 AR antagonists were predicted from their chemical structure to also have potential ER binding activities, i.e. pleiotropic EDCs. In addition, 48 not yet recognized ER binders were in silico identified, which increases the number of potential EDCs that are substances of very high concern (SVHC) in REACH. Thus, the proposed screening models, based only on structure information, have the main aim to prioritize experimental tests for the highlighted compounds with potential estrogenic activities and also to design safer alternatives.

  16. 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.

  17. Bovine renal lipofuscinosis: Prevalence, genetics and impact on milk production and weight at slaughter in Danish cattle

    PubMed Central

    Agerholm, Jørgen S; Christensen, Knud; Nielsen, Søren Saxmose; Flagstad, Pia

    2009-01-01

    Background Bovine renal lipofuscinosis (BRL) is an incidental finding in cattle at slaughter. Condemnation of the kidneys as unfit for human consumption was until recently considered the only implication of BRL. Recent studies have indicated a negative influence on the health of affected animals. The present study investigated the prevalence, genetics and effect of BRL on milk yield and weight at slaughter. Methods BRL status of slaughter cattle was recorded at four abattoirs during a 2-year-period. Data regarding breed, age, genetic descent, milk yield and weight at slaughter were extracted from the Danish Cattle Database. The prevalence of BRL was estimated stratified by breed and age-group. Furthermore, total milk yield, milk yield in last full lactation and weight at slaughter were compared for BRL-affected and non-affected Danish Holsteins and Danish Red cattle. Results 433,759 bovines were slaughtered and 787 of these had BRL. BRL was mainly diagnosed in Danish Red, Danish Holstein and crossbreds. The age of BRL affected animals varied from 11 months to 13 years, but BRL was rarely diagnosed in cattle less than 2 years of age. The total lifelong energy corrected milk (ECM) yields were 3,136 and 4,083 kg higher for BRL affected Danish Red and Danish Holsteins, respectively. However, the median life span of affected animals was 4.9 months longer, and age-corrected total milk yield was 1,284 kg lower for BRL affected Danish Red cows. These cows produced 318 kg ECM less in their last full lactation. Weight at slaughter was not affected by BRL status. The cases occurred in patterns consistent with autosomal recessive inheritance and several family clusters of BRL were found. Analysis of segregation ratios demonstrated the expected ratio for Danish Red cattle, but not for Danish Holsteins. Conclusion The study confirmed that BRL is a common finding in Danish Holsteins and Danish Red cattle at slaughter. The disorder is associated with increased total milk yield due

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

    PubMed

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

    2014-01-01

    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.

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

    PubMed

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

    2015-06-01

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

  20. Synthesis, Biological Evaluation and QSAR Studies of Newer Isoxazole Derivatives.

    PubMed

    Asirvatham, Sahaya; Mahajan, Supriya

    2015-01-01

    A series of newer 3-(4'-methoxyphenyl)-5-substituted phenylisoxazoles derivatives have been synthesized by reacting hydroxylamine hydrochloride with chalcones. The chalcones were formed by reacting different aromatic aldehydes with 4-methoxyacetophenone in presence of aqueos potassium hydroxide (KOH). The purity of all the synthesized compounds was checked by recording their melting points and the retention Factors (Rf) values from thin layer chromatography. The structures of the compounds were characterized by recording their infrared (IR) spectra and confirmed by recording their nuclear magnetic resonance ((1)H NMR) spectra. The acute toxicity study was carried out on all the synthesized compounds and they were screened for their antiinflammatory activity by carrageenan induced rat paw edema method. Anti-inflammatory studies showed statistically significant activity when compared to the control, indomethacin. The two most potent compounds giving good anti-inflammatory activity were further evaluated for their antiulcer activity. The compounds were subjected to quantitative structure activity relationships (QSAR) studies. A close correlation between the observed and the predicted anti-inflammatory activity (Log % inhibition) for the compounds indicated the development of the best QSAR model. The synthesized compounds were found to be non-ulcerogenic as compared to the standard, aspirin.

  1. Universal Approach for Structural Interpretation of QSAR/QSPR Models.

    PubMed

    Polishchuk, Pavel G; Kuz'min, Victor E; Artemenko, Anatoly G; Muratov, Eugene N

    2013-10-01

    In this paper we offer a novel approach for the structural interpretation of QSAR models. The major advantage of our developed methodology is its universality, i.e., it can be applied to any QSAR/QSPR model irrespective of chemical descriptors and machine learning methods applied. This universality was achieved by using only the information obtained from substructures of the compounds of interest to interpret model outcomes. Reliability of the offered approach was confirmed by the results of three case studies, including end-points of different types (continuous and binary classification) and nature (solubility, mutagenicity, and inhibition of Transglutaminase 2), various fragment and whole-molecule descriptors (Simplex and Dragon), and multiple modeling techniques (partial least squares, random forest, and support vector machines). We compared the global contributions of molecular fragments obtained using our methodology with known SAR rules derived experimentally. In all cases high concordance between our interpretation and results published by others was observed. Although the proposed interpretation approach could be easily extended to any type of descriptors, we would recommend using Simplex descriptors to achieve a larger variety of investigated molecular fragments. The developed approach is a good tool for interpretation of such "black box" models like random forest, neural networks, etc. Analysis of fragment global contributions and their deviation across a dataset could be useful for the identification of key fragments and structural alerts. This information could be helpful to maximize the positive influence of structural surroundings on the given fragment and to decrease the negative effects. PMID:27480236

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

    PubMed

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

    2015-06-01

    In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. PMID:27490381

  3. Choosing feature selection and learning algorithms in QSAR.

    PubMed

    Eklund, Martin; Norinder, Ulf; Boyer, Scott; Carlsson, Lars

    2014-03-24

    Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

  4. SAR and QSAR of the antioxidant activity of flavonoids.

    PubMed

    Amić, Dragan; Davidović-Amić, Dusanka; Beslo, Drago; Rastija, Vesna; Lucić, Bono; Trinajstić, Nenad

    2007-01-01

    Flavonoids are a group of naturally occurring phytochemicals abundantly present in fruits, vegetables, and beverages such as wine and tea. In the past two decades, flavonoids have gained enormous interest because of their beneficial health effects such as anti-inflammatory, cardio-protective and anticancer activities. These findings have contributed to the dramatic increase in the consumption and use of dietary supplements containing high concentrations of plant flavonoids. The pharmacological effect of flavonoids is mainly due to their antioxidant activity and their inhibition of certain enzymes. In spite of abundant data, structural requirements and mechanisms underlying these effects have not been fully understood. This review presents the current knowledge about structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs) of the antioxidant activity of flavonoids. SAR and QSAR can provide useful tools for revealing the nature of flavonoid antioxidant action. They may also help in the design of new and efficient flavonoids, which could be used as potential therapeutic agents.

  5. Sorption: Equilibrium partitioning and QSAR development using molecular predictors

    SciTech Connect

    Means, J.C.

    1994-12-31

    Sorption of chemical contaminants to sediments and soils has long been a subject of intensive investigation and QSAR development. Progressing the development of organic carbon-normalized, equilibrium partition constants (Koc) have greatly advanced the prediction of environmental fate. Integration of observed experimental results with thermodynamic modeling of compound behavior, based upon concepts of phase activities and fugacity have placed these QSARs on a firm theoretical base. An increasing spectrum of compound properties such as solubility, chemical activity, molecular surface area and other molecular topological indices have been evaluated for their utility as predictors of sorption properties. Questions concerning the effects of nonequilibrium states, hysteresis or irreversibility in desorption kinetics and equilibria, and particle-concentrations effects upon equilibrium constants as they affect fate predictions remain areas of contemporary investigation. These phenomena are considered and reviewed. The effects of modifying factors such as the effects of salinity or the presence of co-solvents may alter predicted fate of a compound. Competitive sorption with mobile microparticulate or colloidal phases may also impact OSAR predictions. Research on the role of both inorganic and organic-rich colloidal phases as a modifying influence on soil/sediment equilibrium partitioning theory is summarized.

  6. Universal Approach for Structural Interpretation of QSAR/QSPR Models.

    PubMed

    Polishchuk, Pavel G; Kuz'min, Victor E; Artemenko, Anatoly G; Muratov, Eugene N

    2013-10-01

    In this paper we offer a novel approach for the structural interpretation of QSAR models. The major advantage of our developed methodology is its universality, i.e., it can be applied to any QSAR/QSPR model irrespective of chemical descriptors and machine learning methods applied. This universality was achieved by using only the information obtained from substructures of the compounds of interest to interpret model outcomes. Reliability of the offered approach was confirmed by the results of three case studies, including end-points of different types (continuous and binary classification) and nature (solubility, mutagenicity, and inhibition of Transglutaminase 2), various fragment and whole-molecule descriptors (Simplex and Dragon), and multiple modeling techniques (partial least squares, random forest, and support vector machines). We compared the global contributions of molecular fragments obtained using our methodology with known SAR rules derived experimentally. In all cases high concordance between our interpretation and results published by others was observed. Although the proposed interpretation approach could be easily extended to any type of descriptors, we would recommend using Simplex descriptors to achieve a larger variety of investigated molecular fragments. The developed approach is a good tool for interpretation of such "black box" models like random forest, neural networks, etc. Analysis of fragment global contributions and their deviation across a dataset could be useful for the identification of key fragments and structural alerts. This information could be helpful to maximize the positive influence of structural surroundings on the given fragment and to decrease the negative effects.

  7. QSAR study of anti-prion activity of 2-aminothiazoles

    PubMed Central

    Mandi, Prasit; Nantasenamat, Chanin; Srungboonmee, Kakanand; Isarankura-Na-Ayudhya, Chartchalerm; Prachayasittikul, Virapong

    2012-01-01

    2-aminothiazoles is a class of compounds capable of treating life-threatening prion diseases. QSAR studies on a set of forty-seven 2-aminothiazole derivatives possessing anti-prion activity were performed using multivariate analysis, which comprised of multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). The results indicated that MLR afforded reasonable performance with a correlation coefficient (r) and root mean squared error (RMSE) of 0.9073 and 0.2977, respectively, as obtained from leave-one-out cross-validation (LOO-CV). More sophisticated learning methods such as SVM provided models with the highest accuracy with r and RMSE of 0.9471 and 0.2264, respectively, while ANN gave reasonable performance with r and RMSE of 0.9023 and 0.3043, respectively, as obtained LOO-CV calculations. Descriptor analysis from the regression coefficients of the MLR model suggested that compounds should be asymmetrical molecule with low propensity to form hydrogen bonds and high frequency of N content at topological distance 02 in order to provide good activities. Insights from QSAR studies is anticipated to be useful in the design of novel derivatives based on the 2-aminothiazole scaffold as potent therapeutic agents against prion diseases. PMID:27418919

  8. Native Speakers' Judgments of Second Language Danish.

    ERIC Educational Resources Information Center

    Jorgensen, J. N.; Quist, P.

    2001-01-01

    Examines native speakers' reactions to the second language Danish of young Bilingual Turkish-Danish school students. Respondents were asked to evaluate the quality of the Danish of these students on the basis of tape recorded excerpts. Overall, respondents evaluated all speakers more negatively when they considered them to be nonnative Danes, but…

  9. Tuning hERG out: Antitarget QSAR Models for Drug Development

    PubMed Central

    Braga, Rodolpho C.; Alves, Vinícius M.; Silva, Meryck F. B.; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Andrade, Carolina H.

    2015-01-01

    Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg). PMID:24805060

  10. 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...

  11. 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...

  12. Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity.

    PubMed

    Lin, Zhifen; Zhong, Ping; Yin, Kedong; Wang, Liansheng; Yu, Hongxia

    2003-08-01

    A QSAR model is successfully proposed to predict the toxicity effect on Photobacterium phosphoreum by nonpolar-narcotic-chemical mixtures and/or polar-narcotic-chemical mixtures. For nonpolar-narcotic-chemical mixtures and polar-narcotic-chemical mixtures, their corresponding hydrophobicity-based QSAR models are derived from regression analysis. Comparison of these two QSAR models make us believe that it is the joint effect of hydrogen bond in polar-narcotic-chemical mixture that leads to the difference between these two models. Such joint effect of hydrogen bond can be quantified as AMH and BMH by using the different partition coefficients of mixtures in various organic phase/water systems. And the regression analysis results convinced us that the introduction of AMH does improve the quality of the QSAR model with r2=0.948, S.E.=0.166 and F=745.201 at P=0.000 for total 84 mixtures.

  13. 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 corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function.

  14. 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

  15. 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.

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

    PubMed Central

    2014-01-01

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

  17. Biofuel Database

    National Institute of Standards and Technology Data Gateway

    Biofuel Database (Web, free access)   This database brings together structural, biological, and thermodynamic data for enzymes that are either in current use or are being considered for use in the production of biofuels.

  18. A QSAR for the prediction of rate constants for the reaction of VOCs with nitrate radicals.

    PubMed

    Schindler, Michael

    2016-07-01

    A QSAR for the prediction of rate constants for the degradation of volatile organic compounds by nitrate radicals is developed using the Partial Least Squares technique. The QSAR is based on experimental data published in the literature for 260 compounds. They are modeled by a set of calculated descriptors from standard descriptor generation tools and from quantum chemistry. Out of several diversity-based partitionings of the data set a diverse set of 99 compounds turned out to be the optimum choice with regard to simplicity and performance. The final QSAR model is characterized by r(2) = 0.831 (fit) and q(2) = 0.823 (prediction), and by an r(2)pred = 0.862 for the n = 155 external validation set. The QSAR needs 3 latent variables. The most important descriptors for the QSAR are the ionization potential, obtained from density functional theory, and the energy of the highest occupied molecular orbital, which are modulated by fingerprints indicating the presence of specific molecular fragments like functional groups or ring systems. The applicability domain of the new QSAR was studied for some compound classes which are important for the crop protection industry, including (di)hydroxbenzenes and heterocyclic compounds. PMID:27037771

  19. A QSAR for the prediction of rate constants for the reaction of VOCs with nitrate radicals.

    PubMed

    Schindler, Michael

    2016-07-01

    A QSAR for the prediction of rate constants for the degradation of volatile organic compounds by nitrate radicals is developed using the Partial Least Squares technique. The QSAR is based on experimental data published in the literature for 260 compounds. They are modeled by a set of calculated descriptors from standard descriptor generation tools and from quantum chemistry. Out of several diversity-based partitionings of the data set a diverse set of 99 compounds turned out to be the optimum choice with regard to simplicity and performance. The final QSAR model is characterized by r(2) = 0.831 (fit) and q(2) = 0.823 (prediction), and by an r(2)pred = 0.862 for the n = 155 external validation set. The QSAR needs 3 latent variables. The most important descriptors for the QSAR are the ionization potential, obtained from density functional theory, and the energy of the highest occupied molecular orbital, which are modulated by fingerprints indicating the presence of specific molecular fragments like functional groups or ring systems. The applicability domain of the new QSAR was studied for some compound classes which are important for the crop protection industry, including (di)hydroxbenzenes and heterocyclic compounds.

  20. Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity.

    PubMed

    Luo, Man; Wang, Xiang S; Tropsha, Alexander

    2016-01-01

    Ligand based virtual screening (LBVS) approaches could be broadly divided into those relying on chemical similarity searches and those employing Quantitative Structure-Activity Relationship (QSAR) models. We have compared the predictive power of these approaches using some datasets of compounds tested against several G-Protein Coupled Receptors (GPCRs). The k-Nearest Neighbors (kNN) QSAR models were built for known ligands of each GPCR target independently, with a fraction of tested ligands for each target set aside as a validation set. The prediction accuracies of QSAR models for making active/inactive calls for compounds in both training and validation sets were compared to those achieved by the Prediction of Activity Spectra for Substances' (PASS) and the Similarity Ensemble Approach (SEA) tools both available online. Models developed with the kNN QSAR method showed the highest predictive power for almost all tested GPCR datasets. The PASS software, which incorporates multiple end-point specific QSAR models demonstrated a moderate predictive power, while SEA, a chemical similarity based approach, had the lowest prediction power. Our studies suggest that when sufficient amount of data is available to develop and rigorously validate QSAR models such models should be chosen as the preferred virtual screening tool in ligand-based computational drug discovery as compared to chemical similarity based approaches. PMID:27491652

  1. IDENTIFICATION OF PUTATIVE ESTROGEN RECEPTOR-MEDIATED ENDOCRINE DISRUPTING CHEMICALS USING QSAR- AND STRUCTURE-BASED VIRTUAL SCREENING APPROACHES

    PubMed Central

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh; Zhu, Hao; Afantitis, Antreas; Mouchlis, Varnavas D.; Melagraki, Georgia; Rusyn, Ivan; Tropsha, Alexander

    2013-01-01

    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 Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=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), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. PMID:23707773

  2. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

    PubMed

    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(2)=0.71, STL R(2)=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.

  3. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

    PubMed

    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(2)=0.71, STL R(2)=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. PMID:23707773

  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. Database Administrator

    ERIC Educational Resources Information Center

    Moore, Pam

    2010-01-01

    The Internet and electronic commerce (e-commerce) generate lots of data. Data must be stored, organized, and managed. Database administrators, or DBAs, work with database software to find ways to do this. They identify user needs, set up computer databases, and test systems. They ensure that systems perform as they should and add people to the…

  6. Nature and Nationhood: Danish Perspectives

    ERIC Educational Resources Information Center

    Schnack, Karsten

    2009-01-01

    In this paper, I shall discuss Danish perspectives on nature, showing the interdependence of conceptions of "nature" and "nationhood" in the formations of a particular cultural community. Nature, thus construed, is never innocent of culture and cannot therefore simply be "restored" to some pristine, pre-lapsarian state. On the other hand,…

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

    PubMed Central

    2012-01-01

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

  8. 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.

  9. Synthesis, antifungal activity, and QSAR study of novel trichodermin derivatives.

    PubMed

    Cheng, Jing-Li; Zheng, Min; Yao, Ting-Ting; Li, Xiao-Liang; Zhao, Jin-Hao; Xia, Min; Zhu, Guo-Nian

    2015-01-01

    In an attempt to discover more potential antifungal agents, in this study, 21 novel trichodermin derivatives containing conjugated oxime ester (5a-5u) were designed and synthesized and were screened for in vitro antifungal activity. The bioassay tests showed that some of them exhibited good inhibitory activity against the tested pathogenic fungi. Compound 5a exhibited better activity against Pyricularia oryzae and Sclerotonia sclerotiorum than trichodermin, and compound 5j showed particular activity against P.oryzae and Botrytis cinerea. The quantitative structure-activity relationship (QSAR) indicated that log P and hardness were two critical parameters for the biological activities. The result suggested that these would be potential lead compounds for the development of fungicides with further structure modification. PMID:25290081

  10. QSAR and pharmacophore modeling of natural and synthetic antimalarial prodiginines.

    PubMed

    Singh, Baljinder; Vishwakarma, Ram A; Bharate, Sandip B

    2013-09-01

    Prodiginines are a family of linear and cyclic oligopyrrole red-pigmented compounds possessing antibacterial, anticancer and immunosuppressive activities and are produced by actinomycetes and other eubacteria. Recently, prodiginines have been reported to possess potent in vitro as well as in vivo antimalarial activity against chloroquine sensitive D6 and multi-drug resistant Dd2 strains of Plasmodium falciparum. In the present paper, a QSAR and pharmacophore modeling for a series of natural and synthetic prodiginines was performed to find out structural features which are crucial for antimalarial activity against these D6 and Dd2 Plasmodium strains. The study indicated that inertia moment 2 length, Kier Chi6 (path) index, kappa 3 index and Wiener topological index plays important role in antimalarial activity against D6 strain whereas descriptors inertia moment 2 length, ADME H-bond donors, VAMP polarization XX component and VAMP quadpole XZ component play important role in antimalarial activity against Dd2 strain. Furthermore, a five-point pharmacophore (ADHRR) model with one H-bond acceptor (A), one H-bond donor (D), one hydrophobic group (H) and two aromatic rings (R) as pharmacophore features was developed for D6 strain by PHASE module of Schrodinger suite. Similarly a six-point pharmacophore AADDRR was developed for Dd2 strain activity. All developed QSAR models showed good correlation coefficient (r² > 0.7), higher F value (F >20) and excellent predictive power (Q² > 0.6). Developed models will be highly useful for predicting antimalarial activity of new compounds and could help in designing better molecules with enhanced antimalarial activity. Furthermore, calculated ADME properties indicated drug-likeness of prodiginines.

  11. Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

    PubMed

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-08-15

    A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs.

  12. Comparative QSAR- and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds.

    PubMed

    Karakoc, Emre; Sahinalp, S Cenk; Cherkasov, Artem

    2006-01-01

    A number of binary QSAR models have been developed using methods of artificial neural networks, k-nearest neighbors, linear discriminative analysis, and multiple linear regression and have been compared for their ability to recognize five types of chemical compounds that include conventional drugs, inactive druglikes, antimicrobial substituents, and bacterial and human metabolites. Thus, 20 binary classifiers have been created using a variety of 'inductive' and traditional 2D QSAR descriptors which allowed up to 99% accurate separation of the studied groups of activities. The comparison of the performance by four computational approaches demonstrated that the neural nets result in generally more accurate predictions, followed closely by k-nearest neighbors methods. It has also been demonstrated that complementation of 'inductive' descriptors with conventional QSAR parameters does not generally improve the quality of resulting solutions, conforming high predictive ability of 'inductive' variables. The conducted comparative QSAR analysis based on a novel linear optimization approach has helped to identify the extent of overlapping between the studied groups of compounds, such as cross-recognition of bacterial metabolites and antimicrobial compounds reflecting their immanent resemblance and similar origin. Human metabolites have been characterized as a very distinctive class of substances, separated from all other groups in the descriptors space and exhibiting different QSAR behavior. The analysis of unique structural fragments and substituents revealed inhomogeneous scale-free organization of human metabolites illustrating the fact that certain molecular scaffolds (such as sugars and nucleotides) may be strongly favored by natural evolution. The established scale-free organization of human metabolites has been contemplated as a factor of their unique positioning in the descriptors space and their distinctive QSAR properties. It is anticipated that the study may bring

  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. 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. PMID:24738471

  15. 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.

  16. In silico screening for identification of novel β-1,3-glucan synthase inhibitors using pharmacophore and 3D-QSAR methodologies.

    PubMed

    Meetei, Potshangbam Angamba; Rathore, R S; Prabhu, N Prakash; Vindal, Vaibhav

    2016-01-01

    The enzyme β-1,3-glucan synthase, which catalyzes the synthesis of β-1,3-glucan, an essential and unique structural component of the fungal cell wall, has been considered as a promising target for the development of less toxic anti-fungal agents. In this study, a robust pharmacophore model was developed and structure activity relationship analysis of 42 pyridazinone derivatives as β-1,3-glucan synthase inhibitors were carried out. A five-point pharmacophore model, consisting of two aromatic rings (R) and three hydrogen bond acceptors (A) was generated. Pharmacophore based 3D-QSAR model was developed for the same reported data sets. The generated 3D-QSAR model yielded a significant correlation coefficient value (R (2) = 0.954) along with good predictive power confirmed by the high value of cross-validated correlation coefficient (Q (2) = 0.827). Further, the pharmacophore model was employed as a 3D search query to screen small molecules database retrieved from ZINC to select new scaffolds. Finally, ADME studies revealed the pharmacokinetic efficiency of these compounds. PMID:27429875

  17. Danish North Sea crude assayed

    SciTech Connect

    Rhodes, A.K.

    1994-09-12

    Danish North Sea blend was assayed earlier this year. The light, sweet crude comprises crude oil from 10 fields. The crude is piped from offshore production facilities to the A/S Dansk Shell refinery at Fredericia, Denmark. Fig. 1 shows the boiling point curve for the crude, and Fig. 2 illustrates the metals content (vanadium, nickel, and iron), as a function of distillation temperature. The table lists properties of the crude and its fractions.

  18. (Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission.

    PubMed

    Powley, Mark W

    2015-03-01

    (Quantitative) structure activity relationship [(Q)SAR] modeling is the primary tool used to evaluate the mutagenic potential associated with drug impurities. General recommendations regarding the use of (Q)SAR in regulatory decision making have recently been provided in the ICH M7 guideline. Although (Q)SAR alone is capable of achieving reasonable sensitivity and specificity, reliance on a simple positive or negative prediction can be problematic. The key to improving (Q)SAR performance is to integrate supporting information, also referred to as expert knowledge, into the final conclusion. In the regulatory context, expert knowledge is intended to (1) maximize confidence in a (Q)SAR prediction, (2) provide rationale to supersede a positive or negative (Q)SAR prediction, or (3) provide a basis for assessing mutagenicity in absence of a (Q)SAR prediction. Expert knowledge is subjective and is associated with great variability in regards to content and quality. However, it is still a critical component of impurity evaluations and its utility is acknowledged in the ICH M7 guideline. The current paper discusses the use of expert knowledge to support regulatory decision making, describes case studies, and provides recommendations for reporting data from (Q)SAR evaluations.

  19. Database Manager

    ERIC Educational Resources Information Center

    Martin, Andrew

    2010-01-01

    It is normal practice today for organizations to store large quantities of records of related information as computer-based files or databases. Purposeful information is retrieved by performing queries on the data sets. The purpose of DATABASE MANAGER is to communicate to students the method by which the computer performs these queries. This…

  20. Maize databases

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This chapter is a succinct overview of maize data held in the species-specific database MaizeGDB (the Maize Genomics and Genetics Database), and selected multi-species data repositories, such as Gramene/Ensembl Plants, Phytozome, UniProt and the National Center for Biotechnology Information (NCBI), ...

  1. Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

    PubMed

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

    2016-01-01

    Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids. PMID:27160290

  2. Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

    PubMed

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

    2016-01-01

    Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids.

  3. Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models.

    PubMed

    Toplak, Marko; Močnik, Rok; Polajnar, Matija; Bosnić, Zoran; Carlsson, Lars; Hasselgren, Catrin; Demšar, Janez; Boyer, Scott; Zupan, Blaž; Stålring, Jonna

    2014-02-24

    The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package ( https://bitbucket.org/biolab/orange-reliability ) to the Orange data mining suite. PMID:24490838

  4. QSAR models for estimating properties of persistent organic pollutants required in evaluation of their environmental fate and risk.

    PubMed

    Sabljic, A

    2001-04-01

    The molecular connectivity indices (MCIs) have been successfully used for over 20 years in quantitative structure activity relationships (QSAR) modelling in various areas of physics, chemistry, biology, drug design, and environmental sciences. With this review, we hope to assist present and future QSAR practitioners to apply MCIs more wisely and more critically. First, we have described the methods of calculation and systematics of MCIs. This section should be helpful in rational selection of MCIs for QSAR modelling. Then we have presented our long-term experience in the application of MCIs through several characteristic and successful QSAR models for estimating partitioning and chromatographic properties of persistent organic pollutants (POPs). We have also analysed the trends in calculated MCIs and discussed their physical interpretation. In conclusion, several practical recommendations and warnings, based on our research experience, have been given for the application of MCIs in the QSAR modelling. PMID:11302582

  5. QSAR study and conformational analysis of 4-arylthiazolylhydrazones derived from 1-indanones with anti-Trypanosoma cruzi activity.

    PubMed

    Noguera, Guido J; Fabian, Lucas E; Lombardo, Elisa; Finkielsztein, Liliana

    2015-10-12

    A set of 4-arylthiazolylhydrazones derived from 1-indanones (TZHs) previously synthesized and assayed against Trypanosoma cruzi, the causative agent of Chagas disease, were explored in terms of conformational analysis. We found that TZHs can adopt four minimum energy conformations: cis (A, B and C) and trans. The possible bioactive conformation was selected by a 3D-QSAR model. Different molecular parameters were calculated to produce QSAR second-generation models. These QSAR results are discussed in conjunction with conformational analysis from molecular modeling studies. The main factor to determine the activity of the compounds was the partial charge at the N(3) atom (qN3). The predictive ability of the QSAR equations proposed was experimentally validated. The QSAR models developed in this study will be helpful to design novel potent TZHs.

  6. A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes.

    PubMed

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

    2016-06-01

    Experimental determination of the eye irritation potential (EIP) of chemicals is not only tedious, time and resource intensive, it involves cruelty to test animals. In this study, we have established a three-tier QSAR modeling strategy for estimating the EIP of chemicals for the use of pharmaceutical industry and regulatory agencies. Accordingly, a qualitative (binary classification: irritating, non-irritating), semi-quantitative (four-category classification), and quantitative (regression) QSAR models employing the SDT, DTF, and DTB methods were developed for predicting the EIP of chemicals in accordance with the OECD guidelines. Structural features of chemicals responsible for eye irritation were extracted and used in QSAR analysis. The external predictive power of the developed QSAR models were evaluated through the internal and external validation procedures recommended in QSAR literature. In test data, the two and four category classification QSAR models (DTF, DTB) rendered accuracy of >93%, while the regression QSAR models (DTF, DTB) yielded correlation (R(2)) of >0.92 between the measured and predicted EIPs. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except rm(2) in DTF), thus put a high confidence in this analysis. The applicability domain of the constructed QSAR models were defined using the descriptors range and leverage approaches. The QSAR models in this study performed better than any of the previous studies. The results suggest that the developed QSAR models can reliably predict the EIP of diverse chemicals and can be useful tools for screening of candidate molecules in the drug development process.

  7. Genome databases

    SciTech Connect

    Courteau, J.

    1991-10-11

    Since the Genome Project began several years ago, a plethora of databases have been developed or are in the works. They range from the massive Genome Data Base at Johns Hopkins University, the central repository of all gene mapping information, to small databases focusing on single chromosomes or organisms. Some are publicly available, others are essentially private electronic lab notebooks. Still others limit access to a consortium of researchers working on, say, a single human chromosome. An increasing number incorporate sophisticated search and analytical software, while others operate as little more than data lists. In consultation with numerous experts in the field, a list has been compiled of some key genome-related databases. The list was not limited to map and sequence databases but also included the tools investigators use to interpret and elucidate genetic data, such as protein sequence and protein structure databases. Because a major goal of the Genome Project is to map and sequence the genomes of several experimental animals, including E. coli, yeast, fruit fly, nematode, and mouse, the available databases for those organisms are listed as well. The author also includes several databases that are still under development - including some ambitious efforts that go beyond data compilation to create what are being called electronic research communities, enabling many users, rather than just one or a few curators, to add or edit the data and tag it as raw or confirmed.

  8. Descriptors and their selection methods in QSAR analysis: paradigm for drug design.

    PubMed

    Danishuddin; Khan, Asad U

    2016-08-01

    The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure-activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular activity. The usefulness of these descriptors in QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity. In this review, we provide a brief explanation of descriptors and the selection approaches most commonly used in QSAR experiments. In addition, some studies have also demonstrated the positive influence of features selection for any drug development model. PMID:27326911

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

    PubMed

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

    2015-01-01

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

  10. Simplifying complex QSAR's (quantitative structure-activity relationships) in toxicity studies with multivariate statistics

    SciTech Connect

    Niemi, G.J.; McKim, J.M.

    1988-07-01

    During the past several decades many quantitative structure-activity relationships (QSAR's) have been derived from relatively small data sets of chemicals in a homologous series and selected empirical observations. An alternative approach is to analyze large data sets consisting of heterogeneous groups of chemicals and to explore QSAR's among these chemicals for generalized patterns of chemical behavior. The use of exploratory multivariate statistical techniques for simplifying complex QSAR problems is demonstrated through the use of research data on biodegradation and mode of toxic action. In these examples, a large number of explanatory variables were examined to explore which variables might best explain whether a chemical biodegrades or whether a toxic response by an organism can be used to identify a mode of toxic action. In both cases, the procedures reduced the number of potential explanatory variables and generated hypotheses about biodegradation and mode of toxic action for future research without explicitly testing an existing hypothesis.

  11. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

    2016-01-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

  12. 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.

  13. 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.

  14. QSAR for toxicity of organic chemicals to luminescent bacteria

    SciTech Connect

    Devillers, J.; Bintein, S.; Karcher, W.

    1994-12-31

    Over the last decade, there have been increasing pressures to review and reduce the use of laboratory animals for toxicity testing. For ethical and economic reasons, various techniques have been developed and proposed as potential alternatives for some of the whole animal toxicity assays. One assay proposed as an alternative to animal testing is the luminescent bacteria toxicity test, provided under the trade name of Microtox{reg_sign} test. This test has been widely used to estimate the toxicity of agricultural, pharmaceutical, and industrial chemicals producing a large amount of valuable toxicity results. Under these conditions, from a critical analysis of the literature, it has been possible to constitute a large data bank of more than 1,000 organic chemicals for deriving a general QSAR model for the Microtox test. Due to the heterogeneity of the data sets, the molecules were described by means of the modified autocorrelation method. The autocorrelation vectors were generated from atomic contributions encoding the hydrophobicity of the molecules. The validity of the model has been widely discussed and its implications in terms of hazard assessment have been also underlined.

  15. An automated PLS search for biologically relevant QSAR descriptors.

    PubMed

    Olah, Marius; Bologa, Cristian; Oprea, Tudor I

    2004-01-01

    An automated PLS engine, WB-PLS, was applied to 1632 QSAR series with at least 25 compounds per series extracted from WOMBAT (WOrld of Molecular BioAcTivity). WB-PLS extracts a single Y variable per series, as well as pre-computed X variables from a table. The table contained 2D descriptors, the drug-like MDL 320 keys as implemented in the Mesa A&C Fingerprint module, and in-house generated topological-pharmacophore SMARTS counts and fingerprints. Each descriptor type was treated as a block, with or without scaling. Cross-validation, variable importance on projections (VIP) above 0.8 and q2 > or = 0.3 were applied for model significance. Among cross-validation methods, leave-one-in-seven-out (CV7) is a better measure of model significance, compared to leave-one-out (measuring redundancy) and leave-half-out (too restrictive). SMARTS counts overlap with 2D descriptors (having a more quantitative nature), whereas MDL keys overlap with in-house fingerprints (both are more qualitative). The SMARTS counts is the most effective descriptor system, when compared to the other three. At the individual level, size-related descriptors and topological indices (in the 2D property space), and branched SMARTS, aromatic and ring atom types and halogens are found to be most relevant according to the VIP criterion.

  16. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors

    PubMed Central

    Briard, Jennie G.; Fernandez, Michael; De Luna, Phil; Woo, Tom. K.; Ben, Robert N.

    2016-01-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds. PMID:27216585

  17. QSAR Accelerated Discovery of Potent Ice Recrystallization Inhibitors.

    PubMed

    Briard, Jennie G; Fernandez, Michael; De Luna, Phil; Woo, Tom K; Ben, Robert N

    2016-01-01

    Ice recrystallization is the main contributor to cell damage and death during the cryopreservation of cells and tissues. Over the past five years, many small carbohydrate-based molecules were identified as ice recrystallization inhibitors and several were shown to reduce cryoinjury during the cryopreservation of red blood cells (RBCs) and hematopoietic stems cells (HSCs). Unfortunately, clear structure-activity relationships have not been identified impeding the rational design of future compounds possessing ice recrystallization inhibition (IRI) activity. A set of 124 previously synthesized compounds with known IRI activities were used to calibrate 3D-QSAR classification models using GRid INdependent Descriptors (GRIND) derived from DFT level quantum mechanical calculations. Partial least squares (PLS) model was calibrated with 70% of the data set which successfully identified 80% of the IRI active compounds with a precision of 0.8. This model exhibited good performance in screening the remaining 30% of the data set with 70% of active additives successfully recovered with a precision of ~0.7 and specificity of 0.8. The model was further applied to screen a new library of aryl-alditol molecules which were then experimentally synthesized and tested with a success rate of 82%. Presented is the first computer-aided high-throughput experimental screening for novel IRI active compounds.

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

    PubMed

    Lee, Yunho; von Gunten, Urs

    2012-12-01

    Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. PMID:22939392

  19. The current status and future applicability of quantitative structure-activity relationships (QSARs) in predicting toxicity.

    PubMed

    Cronin, Mark T D

    2002-12-01

    The current status of quantitative structure-activity relationships (QSARs) in predicting toxicity is assessed. Widespread use of these methods to predict toxicity from chemical structure is possible, both by industry to develop new compounds, and also by regulatory agencies. The current use of QSARs is restricted by the lack of suitable toxicity data available for modelling, the suitability of simplistic modelling approaches for the prediction of certain endpoints, and the poor definition and utilisation of the applicability domain of models. Suggestions to resolve these issues are made.

  20. QSAR Studies and Design of Some Tetracyclic 1,4-Benzothiazines as Antimicrobial Agents.

    PubMed

    Mor, S; Nagoria, S; Kumar, A; Kumar, A; Kaushik, C P

    2016-08-01

    A quantitative structure-activity relationship (QSAR) analysis has been performed on a series of 20 tetracyclic 1,4-benzothiazines (1a-1t) with antimicrobial activity to explain the observed biological activity trend on structural basis. Multiple linear regression (MLR) method was employed to establish statistically significant QSAR models. The developed models are robust, predictive and free from chance correlation with good fitting ability and sufficient generalizability. These studies revealed the dominance of WHIM parameters in describing antimicrobial activity of the title compounds. Further, design of some more active compounds is presented. PMID:27389854

  1. Genetic analysis of calf and heifer losses in Danish Holstein.

    PubMed

    Fuerst-Waltl, B; Sørensen, M K

    2010-11-01

    Mortality in dairy cattle is not only relevant with regard to economic losses but also to animal health and welfare. Thus, the aim of this investigation was to explore the genetic background of postnatal mortality in calves and replacement heifers in different age groups until first calving in Danish Holsteins. Records of Danish Holstein heifer calves born in the years 1998 to 2007 were extracted from the Danish Cattle database (Danish Cattle, Skejby, Denmark). The following periods (P) were defined for analyses: P1=d 1 to 30, P2=d 31 to 180, P3=d 181 to 365, P4=d 366 until the day before first calving or a maximum age of 1,200 d if no calving was reported, and the full period P5=d 1 until the day before first calving or a maximum age of 1,200 d if no calving was reported. Records of animals slaughtered or exported within a defined period were set to missing for this and following periods, whereas their records were kept for preceding periods. After further data editing, more than 840,000 calves and heifers born in the years 1998 to 2007 were investigated. Mortality rates were 3.23, 2.66, 0.97, 1.92, and 9.36% for the defined periods P1 to P5, respectively. For the estimation of genetic parameters, linear and threshold sire models were applied. Effects accounted for were the random effects herd × year × season and sire as well as the fixed effects year × month, number of dam's parity (parities >5 were set to 5), calf size, and calving ease. In total, the pedigree consisted of 4,643 sires and 20,821 animals. Heritabilities for the linear model were low, ranging from 0.006 (P3) to 0.042 (P5). Heritabilities estimated by threshold models showed a wider range, from not significantly different from zero for periods with low frequencies to 0.082 for P1. The mortality rate until first calving was higher than the stillbirth rate. Genetic and phenotypic variation seemed to be sufficiently high to genetically improve the trait calf and heifer mortality. Hence, a routine

  2. Nonlinear QSAR modeling for predicting cytotoxicity of ionic liquids in leukemia rat cell line: an aid to green chemicals designing.

    PubMed

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

    2015-08-01

    Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86%, and the regression models yielded correlation (R(2)) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs.

  3. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow).

    PubMed

    Papa, Ester; Villa, Fulvio; Gramatica, Paola

    2005-01-01

    The use of Quantitative Structure-Activity Relationships in assessing the potential negative effects of chemicals plays an important role in ecotoxicology. (LC50)(96h) in Pimephales promelas (Duluth database) is widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity of chemicals classified according to their MOA and in a unique general model. The applied multiple linear regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D, and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap, Y-scrambling) and external statistical validations (by splitting the original data set into training and validation sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked by the leverage approach to verify prediction reliability.

  4. 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

  5. Solubility Database

    National Institute of Standards and Technology Data Gateway

    SRD 106 IUPAC-NIST Solubility Database (Web, free access)   These solubilities are compiled from 18 volumes (Click here for List) of the International Union for Pure and Applied Chemistry(IUPAC)-NIST Solubility Data Series. The database includes liquid-liquid, solid-liquid, and gas-liquid systems. Typical solvents and solutes include water, seawater, heavy water, inorganic compounds, and a variety of organic compounds such as hydrocarbons, halogenated hydrocarbons, alcohols, acids, esters and nitrogen compounds. There are over 67,500 solubility measurements and over 1800 references.

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

    PubMed

    Giesen, Daniel; van Gestel, Cornelis A M

    2013-03-01

    Quantitative structure-activity relationships (QSARs) are an established tool in environmental risk assessment and a valuable alternative to the exhaustive use of test animals under REACH. In this study a QSAR was developed for the toxicity of a series of six chloroanilines to the soil-dwelling collembolan Folsomia candida in standardized natural LUFA2.2 soil. Toxicity endpoints incorporated in the QSAR were the concentrations causing 10% (EC10) and 50% (EC50) reduction in reproduction of F. candida. Toxicity was based on concentrations in interstitial water estimated from nominal concentrations in the soil and published soil-water partition coefficients. Estimated effect concentrations were negatively correlated with the lipophilicity of the compounds. Interstitial water concentrations for both the EC10 and EC50 for four compounds were determined by using solid-phase microextraction (SPME). Measured and estimated concentrations were comparable only for tetra- and pentachloroaniline. With decreasing chlorination the disparity between modelled and actual concentrations increased. Optimisation of the QSAR therefore could not be accomplished, showing the necessity to move from total soil to (bio)available concentration measurements. PMID:23276458

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

    EPA Science Inventory

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

  8. 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.

  9. Consensus hologram QSAR modeling for the prediction of human intestinal absorption.

    PubMed

    Moda, Tiago L; Andricopulo, Adriano D

    2012-04-15

    Consistent in silico models for ADME properties are useful tools in early drug discovery. Here, we report the hologram QSAR modeling of human intestinal absorption using a dataset of 638 compounds with experimental data associated. The final validated models are consistent and robust for the consensus prediction of this important pharmacokinetic property and are suitable for virtual screening applications.

  10. 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.

  11. 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. PMID:27086199

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

    PubMed

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

    2009-07-01

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

  13. 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.

  14. 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.

  15. QSAR analysis of antitumor activities of 3,4-ethylenedioxythiphene derivatives

    NASA Astrophysics Data System (ADS)

    Rastija, Vesna; Bajić, Miroslav; Stolić, Ivana; Krstulović, Luka; Jukić, Marijana; Glavaš-Obrovac, Ljubica

    2015-12-01

    QSAR analysis was performed for the antitumor activity of 27 derivatives of 3,4-ethylenedioxythiophene against six carcinoma cell lines. The best models were obtained with surface area (SAG) in combination with lipohilicity (log P) as descriptors. Results have shown that molecules with smaller solvent accessible surface area and higher lipophilicy should have higher biological activity against carcinoma cell.

  16. 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...

  17. In Silico Study of In Vitro GPCR Assays by QSAR Modeling.

    PubMed

    Mansouri, Kamel; Judson, Richard S

    2016-01-01

    The US EPA's ToxCast program is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays. One goal is to prioritize chemicals for more detailed analyses based on activity in assays that target molecular initiating events (MIEs) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than ToxCast's chemical library. In silico methods such as quantitative structure-activity relationships (QSARs) are proven and cost-effective approaches to predict biological activity for untested chemicals. However, empirical data is needed to build and validate QSARs. ToxCast has developed datasets for about 2000 chemicals ideal for training and testing QSAR models. The overall goal of the present work was to develop QSAR models to fill the data gaps in larger environmental chemical lists. The specific aim of the current work was to build QSAR models for 18 G-protein-coupled receptor (GPCR) assays, part of the aminergic family. 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 square discriminant analysis), SVMs (support vector machines), kNNs (k-nearest neighbors), and PLSs (partial least squares). Genetic algorithms (GAs) were applied as a variable selection technique to select the most predictive molecular descriptors for each assay. N-fold cross-validation (CV) coupled with multi-criteria decision-making fitting criteria was used to evaluate the models. Finally, the models were applied to make predictions within the established chemical space limits

  18. In silico QSAR analysis of quercetin reveals its potential as therapeutic drug for Alzheimer's disease

    PubMed Central

    Islam, Md. Rezaul; Zaman, Aubhishek; Jahan, Iffat; Chakravorty, Rajib; Chakraborty, Sajib

    2013-01-01

    Acetylcholine-esterase (AchE) inhibitors are one of the most potent drug molecules against Alzheimer's disease (AD). But, patients treated with current AchE inhibitors often experience severe side effects. Quercetin is a plant flavonoid compound which can act as AchE inhibitor and it may be a better alternative to current AchE inhibitors in terms of effectiveness with no or fewer side effects. Aims The aim of the study was to compare quercetin with conventional AchE inhibitors to search for a better drug candidate. Methods and materials Physico-chemical properties of conventional drugs and quercetin were predicted using bioinformatics tools. Molecular docking of these compounds on the active site of AchE was performed using AutoDock and comparative analysis was performed. Later, modification on the basic structure of quercetin with different functional groups was done to perform QSAR analysis. Result and discussion Quercetin showed a similar drug likeness score to the conventional drugs. The binding strength for quercetin in the active site of the enzyme was −8.8 kcal/mol, which was considerably higher than binding scores for some of the drugs such as donepezil (binding score −7.9 kcal/mol). Fifteen hydrogen bonds were predicted between quercetin and the enzyme whereas conventional drugs had fewer or even no hydrogen bonds. It implies that quercetin can act as a better inhibitor than conventional drugs. To find out even better inhibitor, similar structures of quercetin were searched through SIMCOMP database and a methylation in the 4-OH position of the molecule showed better binding affinity than parent quercetin. Quantitative structure activity relationship study indicated that O-4 methylation was specifically responsible for better affinity. Conclusion This in silico study has conclusively predicted the superiority of the natural compound quercetin over the conventional drugs as AchE inhibitor and it sets the need for further in-vitro study of this

  19. 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.

  20. Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

    PubMed Central

    Kumar, Raj; Son, Minky; Bavi, Rohit; Lee, Yuno; Park, Chanin; Arulalapperumal, Venkatesh; Cao, Guang Ping; Kim, Hyong-ha; Suh, Jung-keun; Kim, Yong-seong; Kwon, Yong Jung; Lee, Keun Woo

    2015-01-01

    Aim: Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches. Methods: The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations. Results: The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer's randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10. Conclusion: Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors. PMID:26051108

  1. 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...

  2. 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

  3. 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.

  4. INTEGRATING TOXICITY PATHWAY-SPECIFIC IN VITRO TESTING WITH ADVANCED CHEMICAL SELECTION STRATEGIES FOR ENVIRONMENTAL QSAR DEVELOPMENT

    EPA Science Inventory

    Environmental risk assessments often require the evaluation of large numbers of untested chemicals. Chemical assessment protocols that include QSARs have been widely applied for endpoint prediction as well as chemical ranking and prioritization. Approaches are needed to strategi...

  5. Drinking Water Treatability Database (Database)

    EPA Science Inventory

    The drinking Water Treatability Database (TDB) will provide data taken from the literature on the control of contaminants in drinking water, and will be housed on an interactive, publicly-available USEPA web site. It can be used for identifying effective treatment processes, rec...

  6. Multi-target QSAR and docking study of steroids binding to corticosteroid-binding globulin and sex hormone-binding globulin.

    PubMed

    Nikolic, Katarina; Filipic, Slavica; Agbaba, Danica

    2012-12-01

    The QSAR and docking studies were performed on fifty seven steroids with binding affinities for corticosteroid-binding globulin (CBG) and eighty four steroids with binding affinities for sex hormone-binding globulin (SHBG). Since the steroidal compounds have binding affinity for both CBG and SHBG, multi-target QSAR approach was employed to establish a unique QSAR method for simultaneous evaluation of the CBG and SHBG binding affinities. The constitutional, geometrical, physico-chemical and electronic descriptors were computed for the examined structures by use of the Chem3D Ultra 7.0.0, the Dragon 6.0, the MOPAC2009, and the Chemical Descriptors Library (CDL) program. Partial least squares regression (PLSR) has been applied for selection of the most relevant molecular descriptors and QSAR models building. The QSAR (SHGB) model, QSAR model (CBG), and multi-target QSAR model (CBG, SHBG) were created. The multi-target QSAR model (CBG and SHBG) was found to be more effective in describing the CBG and SHBG affinity of steroids in comparison to the one target models (QSAR (SHGB) model, QSAR model (CBG)). The multi-target QSAR study indicated the importance of the electronic descriptor (Mor16v), steric/symmetry descriptors (Eig06_EA(ed)), 2D autocorrelation descriptor (GATS4m), distance distribution descriptor (RDF045m), and atom type fingerprint descriptor (CDL-ATFP 253) in describing the CBG and SHBG affinity of steroidal compounds. Results of the created multi-target QSAR model were in accordance with the performed docking studies. The theoretical study defined physicochemical, electronic and structural requirements for selective and effective binding of steroids to the CBG and SHBG active sites.

  7. 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.

  8. 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. PMID:26474167

  9. 2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods

    PubMed Central

    Ghanbarzadeh, Saeed; Ghasemi, Saeed; Shayanfar, Ali; Ebrahimi-Najafabadi, Heshmatollah

    2015-01-01

    Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models. PMID:26600747

  10. 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

  11. Assessment of the probability of introducing Mycobacterium tuberculosis into Danish cattle herds.

    PubMed

    Foddai, Alessandro; Nielsen, Liza Rosenbaum; Krogh, Kaspar; Alban, Lis

    2015-11-01

    Tuberculosis is a zoonosis caused by Mycobacterium spp. International trade in cattle is regulated with respect to Mycobacterium bovis (M. bovis) but not Mycobacterium tuberculosis (M. tuberculosis), despite that cattle can become infected with both species. In this study we estimated the annual probability (PIntro) of introducing M. tuberculosis into the Danish cattle population, by the import of cattle and/or by immigrants working in Danish cattle herds. Data from 2013 with date, number, and origin of imported live cattle were obtained from the Danish cattle database. Information on immigrants working in Danish cattle herds was obtained through a questionnaire sent to Danish cattle farmers. The gained inputs were fed into three stochastic scenario trees to assess the PIntro for the current and alternative test-and-manage strategies, such as testing of imported animals and/or testing immigrant workers with the tuberculin skin test. We considered the population of Danish farmers and practitioners free of tuberculosis, because in Denmark, the incidence of the disease in humans is low and primarily related to immigrants and socially disadvantaged people. The median annual probability of introducing M. tuberculosis into the Danish cattle population due to imported live cattle was 0.008% (90% P.I.: 0.0007%; 0.03%), while the probability due to immigrant workers was 4.1% (90% P.I.: 0.8%; 12.1%). The median combined probability (PIntro) due to imported cattle plus workers was 4.1% (90% P.I.: 0.8%; 12.6%). Hence, on average at least one introduction each 24 (90% P.I.: 8; 125) years could be expected. Imported live cattle appeared to play a marginal role on the overall annual PIntro, because they represented only approximately 0.2% of the median annual probability. By testing immigrant workers the overall annual PIntro could be reduced to 0.2% (90% P.I.: 0.04%; 0.7%). Thus, testing of immigrant workers could be considered as a risk mitigation strategy to markedly reduce

  12. Assessment of the probability of introducing Mycobacterium tuberculosis into Danish cattle herds.

    PubMed

    Foddai, Alessandro; Nielsen, Liza Rosenbaum; Krogh, Kaspar; Alban, Lis

    2015-11-01

    Tuberculosis is a zoonosis caused by Mycobacterium spp. International trade in cattle is regulated with respect to Mycobacterium bovis (M. bovis) but not Mycobacterium tuberculosis (M. tuberculosis), despite that cattle can become infected with both species. In this study we estimated the annual probability (PIntro) of introducing M. tuberculosis into the Danish cattle population, by the import of cattle and/or by immigrants working in Danish cattle herds. Data from 2013 with date, number, and origin of imported live cattle were obtained from the Danish cattle database. Information on immigrants working in Danish cattle herds was obtained through a questionnaire sent to Danish cattle farmers. The gained inputs were fed into three stochastic scenario trees to assess the PIntro for the current and alternative test-and-manage strategies, such as testing of imported animals and/or testing immigrant workers with the tuberculin skin test. We considered the population of Danish farmers and practitioners free of tuberculosis, because in Denmark, the incidence of the disease in humans is low and primarily related to immigrants and socially disadvantaged people. The median annual probability of introducing M. tuberculosis into the Danish cattle population due to imported live cattle was 0.008% (90% P.I.: 0.0007%; 0.03%), while the probability due to immigrant workers was 4.1% (90% P.I.: 0.8%; 12.1%). The median combined probability (PIntro) due to imported cattle plus workers was 4.1% (90% P.I.: 0.8%; 12.6%). Hence, on average at least one introduction each 24 (90% P.I.: 8; 125) years could be expected. Imported live cattle appeared to play a marginal role on the overall annual PIntro, because they represented only approximately 0.2% of the median annual probability. By testing immigrant workers the overall annual PIntro could be reduced to 0.2% (90% P.I.: 0.04%; 0.7%). Thus, testing of immigrant workers could be considered as a risk mitigation strategy to markedly reduce

  13. A combined pharmacophore modeling, 3D-QSAR and molecular docking study of substituted bicyclo-[3.3.0]oct-2-enes as liver receptor homolog-1 (LRH-1) agonists

    NASA Astrophysics Data System (ADS)

    Lalit, Manisha; Gangwal, Rahul P.; Dhoke, Gaurao V.; Damre, Mangesh V.; Khandelwal, Kanchan; Sangamwar, Abhay T.

    2013-10-01

    A combined pharmacophore modelling, 3D-QSAR and molecular docking approach was employed to reveal structural and chemical features essential for the development of small molecules as LRH-1 agonists. The best HypoGen pharmacophore hypothesis (Hypo1) consists of one hydrogen-bond donor (HBD), two general hydrophobic (H), one hydrophobic aromatic (HYAr) and one hydrophobic aliphatic (HYA) feature. It has exhibited high correlation coefficient of 0.927, cost difference of 85.178 bit and low RMS value of 1.411. This pharmacophore hypothesis was cross-validated using test set, decoy set and Cat-Scramble methodology. Subsequently, validated pharmacophore hypothesis was used in the screening of small chemical databases. Further, 3D-QSAR models were developed based on the alignment obtained using substructure alignment. The best CoMFA and CoMSIA model has exhibited excellent rncv2 values of 0.991 and 0.987, and rcv2 values of 0.767 and 0.703, respectively. CoMFA predicted rpred2 of 0.87 and CoMSIA predicted rpred2 of 0.78 showed that the predicted values were in good agreement with the experimental values. Molecular docking analysis reveals that π-π interaction with His390 and hydrogen bond interaction with His390/Arg393 is essential for LRH-1 agonistic activity. The results from pharmacophore modelling, 3D-QSAR and molecular docking are complementary to each other and could serve as a powerful tool for the discovery of potent small molecules as LRH-1 agonists.

  14. Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors

    PubMed Central

    Sun, Xian-qiang; Chen, Lei; Li, Yao-zong; Li, Wei-hua; Liu, Gui-xia; Tu, Yao-quan; Tang, Yun

    2014-01-01

    Aim: To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors. Methods: One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures. Results: Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands. Conclusion: The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures. PMID:24335842

  15. 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

  16. Monitoring of equine health in Denmark: the importance, purpose, research areas and content of a future database.

    PubMed

    Hartig, Wendy; Houe, Hans; Andersen, Pia Haubro

    2013-04-01

    The plentiful data on Danish horses are currently neither organized nor easily accessible, impeding register-based epidemiological studies on Danish horses. A common database could be beneficial. In principle, databases can contain a wealth of information, but no single database can serve every purpose. Hence the establishment of a Danish equine health database should be preceded by careful consideration of its purpose and content, and stakeholder attitudes should be investigated. The objectives of the present study were to identify stakeholder attitudes to the importance, purpose, research areas and content of a health database for horses in Denmark. A cross-sectional study was conducted with 13 horse-related stakeholder groups in Denmark. The groups surveyed included equine veterinarians, researchers, veterinary students, representatives from animal welfare organizations, horse owners, trainers, farriers, authority representatives, ordinary citizens, and representatives from laboratories, insurance companies, medical equipment companies and pharmaceutical companies. Supplementary attitudes were inferred from qualitative responses. The overall response rate for all stakeholder groups was 45%. Stakeholder group-specific response rates were 27-80%. Sixty-eight percent of questionnaire respondents thought a national equine health database was important. Most respondents wanted the database to contribute to improved horse health and welfare, to be used for research into durability and disease heritability, and to serve as a basis for health declarations for individual horses. The generally preferred purpose of the database was thus that it should focus on horse health and welfare rather than on performance or food safety, and that it should be able to function both at a population and an individual horse level. In conclusion, there is a positive attitude to the establishment of a health database for Danish horses. These results could enrich further reflection on the

  17. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies.

    PubMed

    Gajewicz, Agnieszka; Schaeublin, Nicole; Rasulev, Bakhtiyor; Hussain, Saber; Leszczynska, Danuta; Puzyn, Tomasz; Leszczynski, Jerzy

    2015-05-01

    The production of nanomaterials increases every year exponentially and therefore the probability these novel materials that they could cause adverse outcomes for human health and the environment also expands rapidly. We proposed two types of mechanisms of toxic action that are collectively applied in a nano-QSAR model, which provides governance over the toxicity of metal oxide nanoparticles to the human keratinocyte cell line (HaCaT). The combined experimental-theoretical studies allowed the development of an interpretative nano-QSAR model describing the toxicity of 18 nano-metal oxides to the HaCaT cell line, which is a common in vitro model for keratinocyte response during toxic dermal exposure. The comparison of the toxicity of metal oxide nanoparticles to bacteria Escherichia coli (prokaryotic system) and a human keratinocyte cell line (eukaryotic system), resulted in the hypothesis that different modes of toxic action occur between prokaryotic and eukaryotic systems.

  18. QSAR and QM/MM approaches applied to drug metabolism prediction.

    PubMed

    Braga, R C; Andrade, C H

    2012-06-01

    In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In recent years, large number of in silico approaches for metabolism prediction have been developed and reported. Among these methods, QSAR models and combined quantum mechanics/molecular mechanics (QM/MM) methods for predicting drug metabolism have undergone significant advances. This review provides a perspective of the utility of QSAR and QM/MM approaches on drug metabolism prediction, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.

  19. In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.

    PubMed

    Mombelli, Enrico; Raitano, Giuseppa; Benfenati, Emilio

    2016-01-01

    Information on genotoxicity is an essential piece of information gathering for a comprehensive toxicological characterization of chemicals. Several QSAR models that can predict Ames genotoxicity are freely available for download from the Internet and they can provide relevant information for the toxicological profiling of chemicals. Indeed, they can be straightforwardly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA.Nevertheless, and despite the ease of use of these models, the scientific challenge is to assess the reliability of information that can be obtained from these tools. This chapter provides instructions on how to use freely available QSAR models and on how to interpret their predictions.

  20. 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.

  1. QSAR Modeling of Imbalanced High-Throughput Screening Data in PubChem

    PubMed Central

    2015-01-01

    Many of the structures in PubChem are annotated with activities determined in high-throughput screening (HTS) assays. Because of the nature of these assays, the activity data are typically strongly imbalanced, with a small number of active compounds contrasting with a very large number of inactive compounds. We have used several such imbalanced PubChem HTS assays to test and develop strategies to efficiently build robust QSAR models from imbalanced data sets. Different descriptor types [Quantitative Neighborhoods of Atoms (QNA) and “biological” descriptors] were used to generate a variety of QSAR models in the program GUSAR. The models obtained were compared using external test and validation sets. We also report on our efforts to incorporate the most predictive of our models in the publicly available NCI/CADD Group Web services (http://cactus.nci.nih.gov/chemical/apps/cap). PMID:24524735

  2. In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.

    PubMed

    Mombelli, Enrico; Raitano, Giuseppa; Benfenati, Emilio

    2016-01-01

    Information on genotoxicity is an essential piece of information gathering for a comprehensive toxicological characterization of chemicals. Several QSAR models that can predict Ames genotoxicity are freely available for download from the Internet and they can provide relevant information for the toxicological profiling of chemicals. Indeed, they can be straightforwardly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA.Nevertheless, and despite the ease of use of these models, the scientific challenge is to assess the reliability of information that can be obtained from these tools. This chapter provides instructions on how to use freely available QSAR models and on how to interpret their predictions. PMID:27311463

  3. QSAR Study on Thiazolidine-2,4-dione Derivatives for Antihyperglycemic Activity.

    PubMed

    Prashantha Kumar, B R; Nanjan, M J

    2008-09-01

    A set of seventy four molecules belonging to the class of thioglitazones were subjected to the QSAR analysis for their antihyperglycemic activity. All the molecules were subjected to energy minimization to get 3D structures, followed by conformational analysis to get the conformation of the molecule associated with the least energy and highest stability. Various physico-chemical parameters were then calculated using ALCHEMY 2000 software, namely, thermodynamic parameters, structure-dependant parameters, topological parameters and charge-dependant parameters. Multiple linear regression analysis was carried out on all the molecules. The final equation was developed by choosing optimal combination of descriptors after removing the outliers. Cross validation was performed by leave one out method to arrive at the final QSAR model for the chosen set of molecules to exhibit antihyperglycemic activity. PMID:21394250

  4. Quantitative structure-activity/ecotoxicity relationships (QSAR/QEcoSAR) of a series of phosphonates.

    PubMed

    Petrescu, Alina-Maria; Putz, Mihai V; Ilia, Gheorghe

    2015-11-01

    In this paper the structure-toxicity relationship studies were performed for a series of 60 phosphonates. The toxicity of the compounds was determined by two ways: by quantifying the measured toxicity values, Mlog(1/MRIC50) collected by literature, for rodents species; second by using EcoSAR software version 1.11, for calculating the toxicity for fish species, considered as dependent variables and they were related to structural features obtained by molecular and quantum mechanics calculations. The QSAR/QEcoSAR was validated by multiple linear regression (MLR), although the purpose of this work was not to validate the model proposed, but rather to test the influence of structural parameters of the proposed model QSAR/QEcoSAR. The obtained models showed that the toxicity of phosphonates was influenced by steric and molecular geometry which cause inhibition of cholinesterase activity.

  5. QSAR Study on Thiazolidine-2,4-dione Derivatives for Antihyperglycemic Activity

    PubMed Central

    Prashantha Kumar, B. R.; Nanjan, M. J.

    2008-01-01

    A set of seventy four molecules belonging to the class of thioglitazones were subjected to the QSAR analysis for their antihyperglycemic activity. All the molecules were subjected to energy minimization to get 3D structures, followed by conformational analysis to get the conformation of the molecule associated with the least energy and highest stability. Various physico-chemical parameters were then calculated using ALCHEMY 2000 software, namely, thermodynamic parameters, structure-dependant parameters, topological parameters and charge-dependant parameters. Multiple linear regression analysis was carried out on all the molecules. The final equation was developed by choosing optimal combination of descriptors after removing the outliers. Cross validation was performed by leave one out method to arrive at the final QSAR model for the chosen set of molecules to exhibit antihyperglycemic activity. PMID:21394250

  6. Stackfile Database

    NASA Technical Reports Server (NTRS)

    deVarvalho, Robert; Desai, Shailen D.; Haines, Bruce J.; Kruizinga, Gerhard L.; Gilmer, Christopher

    2013-01-01

    This software provides storage retrieval and analysis functionality for managing satellite altimetry data. It improves the efficiency and analysis capabilities of existing database software with improved flexibility and documentation. It offers flexibility in the type of data that can be stored. There is efficient retrieval either across the spatial domain or the time domain. Built-in analysis tools are provided for frequently performed altimetry tasks. This software package is used for storing and manipulating satellite measurement data. It was developed with a focus on handling the requirements of repeat-track altimetry missions such as Topex and Jason. It was, however, designed to work with a wide variety of satellite measurement data [e.g., Gravity Recovery And Climate Experiment -- GRACE). The software consists of several command-line tools for importing, retrieving, and analyzing satellite measurement data.

  7. 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. PMID:26170251

  8. Design, synthesis, evaluation and QSAR analysis of N(1)-substituted norcymserine derivatives as selective butyrylcholinesterase inhibitors.

    PubMed

    Takahashi, Jun; Hijikuro, Ichiro; Kihara, Takeshi; Murugesh, Modachur G; Fuse, Shinichiro; Kunimoto, Ryo; Tsumura, Yoshinori; Akaike, Akinori; Niidome, Tetsuhiro; Okuno, Yasushi; Takahashi, Takashi; Sugimoto, Hachiro

    2010-03-01

    We synthesized a series of N(1)-substituted norcymserine derivatives 7a-p and evaluated their anti-cholinesterase activities. In vitro evaluation showed that the pyridinylethyl derivatives 7m-o and the piperidinylethyl derivative 7p improved the anti-butyrylcholinesterase activity by approximately threefold compared to N(1)-phenethylnorcymserine (PEC, 2). A quantitative structure-activity relationship (QSAR) study indicated that logS might be a key feature of the improved compounds.

  9. 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. PMID:25937011

  10. 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.

  11. Polychlorinated biphenyls: correlation between in vivo and in vitro quantitative structure-activity relationships (QSARs)

    SciTech Connect

    Leece, B.; Denomme, M.A.; Towner, R.; Li, S.M.A.; Safe, S.

    1985-01-01

    The in vivo quantitative structure-activity relationships (QSARs) for several polychlorinated biphenyls (PCBs) were determined in the immature male Wistar rat. The ED25 and ED50 values for hepatic microsomal aryl hydrocarbon hydroxylase (AHH) and ethoxyresorufin O-deethylase (EROD) induction as well as for body weight loss and for thymic atrophy were determined for nine PCB congeners and 4'-bromo-2,3,4,5-tetrachlorobiphenyl. The most active compounds were the coplanar PCB congeners, 3,3',4,4',5-penta- and 3,3',4,4',5,5'-hexachlorobiphenyl; for example, their ED50 values for body weight loss were 3.25 and 15.1 ..mu..mol/kg, respectively. The in vivo toxicity of the coplanar PCB, 3,3',4,4'-tetrachlorobiphenyl, was significantly lower (ED50 for body weight loss = 730 ..mu..mol/kg) than the values observed for the more highly chlorinated homologs, and this was consistent with the more rapid metabolism of the lower chlorinated congener. The dose-response biologic and toxic effects of several mono-ortho-chloro-substituted analogs of the coplanar PCBs, including 2,3,4,4',5-, 2,3,3',4,4'-, 2',3,4,4',5- and 2,3',4,4',5-penta-, 2,3,3',4,4',5- and 2,3,3',4,4',5-hexachlorobiphenyl were also determined, and members of this group of compounds were all less toxic than 3,3',4,4',5-penta and 3,3',4,4',5,5'-hexachlorobiphenyl. There was a good rank order correlation between the in vivo QSAR data and the in vitro QSAR data and the in vitro QSARs for PCBs that were developed from their relative receptor binding affinities and potencies as inducers of AHH and EROD in rat hepatoma H-4-II E cells in culture.

  12. 3D-QSAR and molecular fragment replacement study on diaminopyrimidine and pyrrolotriazine ALK inhibitors

    NASA Astrophysics Data System (ADS)

    Ke, Zhipeng; Lu, Tao; Liu, Haichun; Yuan, Haoliang; Ran, Ting; Zhang, Yanmin; Yao, Sihui; Xiong, Xiao; Xu, Jinxing; Xu, Anyang; Chen, Yadong

    2014-06-01

    Over expression of anaplastic lymphoma kinase (ALK) has been found in many types of cancer, and ALK is a promising therapeutic target for the treatment of cancer. To obtain new potent inhibitors of ALK, we conducted lead optimization using 3D-QSAR modeling and molecular docking investigation of 2,4-diaminopyrimidines and 2,7-disubstituted-pyrrolo[2,1-f][1,2,4]triazine-based compounds. Three favorable 3D-QSAR models (CoMFA with q2, 0.555; r2, 0.939; CoMSIA with q2, 0.625; r2, 0.974; Topomer CoMFA with q2, 0.557; r2 0.756) have been developed to predict the biological activity of novel compounds. Topomer Search was utilized for virtual screening to obtain suitable fragments. The novel compounds generated by molecular fragment replacement (MFR) were evaluated by Topomer CoMFA prediction, Glide (docking) and further evaluated with CoMFA and CoMSIA prediction. 25 novel 2,7-disubstituted-pyrrolo[2,1-f][1,2,4]triazine derivatives as potential ALK inhibitors were finally obtained. In this paper, a combination of CoMFA, CoMSIA and Topomer CoMFA could obtain favorable 3D-QSAR models and suitable fragments for ALK inhibitors optimization. The work flow which comprised 3D-QSAR modeling, Topomer Search, MFR, molecular docking and evaluating criteria could be applied to de novo drug design and the resulted compounds initiate us to further optimize and design new potential ALK inhibitors.

  13. 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. PMID:22245076

  14. 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.

  15. 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

  16. QSAR for predicting joint toxicity of halogenated benzenes to Dicrateria zhanjiangensis.

    PubMed

    Zeng, Ming; Lin, ZhiFen; Yin, DaQiang; Yin, KeDong

    2008-12-01

    In this study, the toxicity of 49 mixed halogenated benzenes to Dicrateria zhanjiangensis was determined and the partition coefficient of these mixtures was described by using the C(18)-Empore disks/water partition coefficient (K(mix)). According to these data, a simple K(mix)-based QSAR model was successfully used to correlate the toxicity of the mixed halogenated benzenes to D. zhanjiangensis.

  17. Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF)

    SciTech Connect

    Gissi, Andrea; Lombardo, Anna; Roncaglioni, Alessandra; Gadaleta, Domenico; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio; Benfenati, Emilio

    2015-02-15

    The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R{sup 2}) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R{sup 2}>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R{sup 2}{sub ext}=0.59), followed by the EPISuite Meylan model (R{sup 2}{sub ext}=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R{sup 2}=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R{sup 2

  18. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.

    PubMed

    Nikolic, Katarina; Mavridis, Lazaros; Djikic, Teodora; Vucicevic, Jelica; Agbaba, Danica; Yelekci, Kemal; Mitchell, John B O

    2016-01-01

    HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands

  19. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

    PubMed Central

    Nikolic, Katarina; Mavridis, Lazaros; Djikic, Teodora; Vucicevic, Jelica; Agbaba, Danica; Yelekci, Kemal; Mitchell, John B. O.

    2016-01-01

    HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent

  20. Identification of Potent Virtual Leads Specific to S1' Loop of ADAMTS4: Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Dynamic Studies.

    PubMed

    Suganya, P Rathi; Kalva, Sukesh; Saleena, Lilly M

    2016-01-01

    ADAMTS4 (Aggrecanase-1) is an important enzyme, which belongs to ADAMTS family. Aggrecanase-1 is involved in aggrecan degradation of articular cartilage in osteoarthritis and rheumatoid arthritis. Overall variability of S1' domain of ADAMTS4 has been the main selectivity determinant to design the unique inhibitors. 34 inhibitors from Binding database and literature were used to develop the pharmacophore model. The five featured pharmacophore model AHHRR had the best survival score of 3.493 and post-hoc score of 2.545, indicating that the model is highly reliable. The 3D-QSAR acquired had excellent r(2) value of 0.99 and GH score of 0.839. The validated pharmacophore model was used for insilico screening of Asinex and ZINC database for finding the potential lead compounds. ZINC00987406 and ASN04459656 which pose high glide score i.e >7 Kcal/mol and H-bond and hydrophobic interactions in the S1'loop residues of ADAMTS4 were subjected to Molecular Dynamics Simulation studies. Molecular dynamic simulation result indicates that the RMSD and RMSF of backbone atoms for the above complexes were within the limit of 2.0 A˚. These compounds can be potential candidates for osteoarthritis by inhibiting ADAMTS4. PMID:26813685

  1. QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery.

    PubMed

    Pereira, Florbela; Latino, Diogo A R S; Gaudêncio, Susana P

    2015-03-17

    A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database-25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.

  2. De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction.

    PubMed

    Zhang, Yanmin; Liu, Haichun; Jiao, Yu; Yuan, Haoliang; Wang, Fengxiao; Lu, Shuai; Yao, Sihui; Ke, Zhipeng; Tai, Wenting; Jiang, Yulei; Chen, Yadong; Lu, Tao

    2012-11-01

    Vascular endothelial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as promising targets for novel anticancer agents. To achieve new potent inhibitors of KDR, we conducted molecular fragment replacement (MFR) studies for the understanding of 3D-QSAR modeling and the docking investigation of arylphthalazines and 2-((1H-Azol-1-yl)methyl)-N-arylbenzamides-based KDR inhibitors. Two favorable 3D-QSAR models (CoMFA with q(2), 0.671; r(2), 0.969; CoMSIA with q(2), 0.608; r(2), 0.936) have been developed to predict the biological activity of new compounds. The new molecular database generated by MFR was virtually screened using Glide (docking) and further evaluated with CoMFA prediction, protein-ligand interaction fingerprint (PLIF) and ADMET analysis. 44 N-(pyridin-4-ylmethyl)aniline derivatives as novel potential KDR inhibitors were finally obtained. In this paper, the work flow developed could be applied to de novo drug design and virtual screening potential KDR inhibitors, and use hit compounds to further optimize and design new potential KDR inhibitors.

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

    PubMed

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

    2015-10-20

    A novel series of 1,4-naphthoquinones (33-44) tethered by open and closed chain sulfonamide moieties were designed, synthesized and evaluated for their cytotoxic and antimalarial activities. All quinone-sulfonamide derivatives displayed a broad spectrum of cytotoxic activities against all of the tested cancer cell lines including HuCCA-1, HepG2, A549 and MOLT-3. Most quinones (33-36 and 38-43) exerted higher anticancer activity against HepG2 cell than that of the etoposide. The open chain analogs 36 and 42 were shown to be the most potent compounds. Notably, the restricted sulfonamide analog 38 with 6,7-dimethoxy groups exhibited the most potent antimalarial activity (IC₅₀ = 2.8 μM). Quantitative structure-activity relationships (QSAR) study was performed to reveal important chemical features governing the biological activities. Five constructed QSAR models provided acceptable predictive performance (Rcv 0.5647-0.9317 and RMSEcv 0.1231-0.2825). Four additional sets of structurally modified compounds were generated in silico (34a-34d, 36a-36k, 40a-40d and 42a-42k) in which their activities were predicted using the constructed QSAR models. A comprehensive discussion of the structure-activity relationships was made and a set of promising compounds (i.e., 33, 36, 38, 42, 36d, 36f, 42e, 42g and 42f) was suggested for further development as anticancer and antimalarial agents. PMID:26397393

  4. 3D QSAR studies of hydroxylated polychlorinated biphenyls as potential xenoestrogens.

    PubMed

    Ruiz, Patricia; Ingale, Kundan; Wheeler, John S; Mumtaz, Moiz

    2016-02-01

    Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are found in human biological samples and lack of data on their potential estrogenic activity has been a source of concern. We have extended our previous in silico 2D QSAR study through the application of advance techniques such as docking and 3D QSAR to gain insights into their estrogen receptor (ERα) binding. The results support our earlier findings that the hydroxyl group is the most important feature on the compounds; its position, orientation and surroundings in the structure are influential for the binding of OH-PCBs to ERα. This study has also revealed the following additional interactions that influence estrogenicity of these chemicals (a) the aromatic interactions of the biphenyl moieties with the receptor, (b) hydrogen bonding interactions of the p-hydroxyl group with key amino acids ARG394 and GLU353, (c) low or no electronegative substitution at para-positions of the p-hydroxyl group, (d) enhanced electrostatic interactions at the meta position on the B ring, and (e) co-planarity of the hydroxyl group on the A ring. In combination the 2D and 3D QSAR approaches have led us to the support conclusion that the hydroxyl group is the most important feature on the OH-PCB influencing the binding to estrogen receptors, and have enhanced our understanding of the mechanistic details of estrogenicity of this class of chemicals. Such in silico computational methods could serve as useful tools in risk assessment of chemicals. PMID:26598992

  5. 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.

  6. 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.

  7. 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.

  8. 3D-QSAR study of benzotriazol-1-yl carboxamide scaffold as monoacylglycerol lipase inhibitors

    PubMed Central

    Afzal, Obaid; Kumar, Suresh; Kumar, Rajiv; Jaggi, Manu; Bawa, Sandhya

    2014-01-01

    Purpose: The purpose of this study is to build up the 3D pharmacophore of Monoacylglycerol lipase (MAGL) inhibitor and to provide the basis to design the novel and potent MAGL inhibitors. Material and Method: A 3D-QSAR study on benztriazol-1-yl carboxamide derivatives as monoacylglycerol lipase (MAGL) inhibitors was successfully performed by means of pharmacophore mapping using PHASE 3.5 module of Schrφdinger-9.4. Result: The 3D-QSAR obtained from APRRR-105 hypothesis was found to be statistically good with r2 = 0.9228 and q2 = 0.871, taking PLS factor 4. The statistical significance of the model was also confirmed by a high value of Fisher's ratio of 82.8 and a very low value of root-mean-square error (RMSE) 0.2564. Another parameter which signifies the model predictivity is Pearson R. Its value of 0.9512 showed that the correlation between predicted and observed activities for the test set compounds is excellent. Conclusion: The study suggested that one H-bond acceptor, one positive center, and proper positioning of hydrophobic groups near the distal aromatic ring C are the crucial determinants for MAGL inhibition. Thus, it can be assumed that the present QSAR analysis is enough to demonstrate MAGL inhibition with the help of APRRR-105 hypothesis and will be helpful in designing novel and potent MAGL inhibitors. PMID:25400409

  9. 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.

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

    PubMed Central

    Alam, Sarfaraz; Khan, Feroz

    2014-01-01

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

  11. Is conformation a fundamental descriptor in QSAR? A case for halogenated anesthetics.

    PubMed

    Guimarães, Maria C; Duarte, Mariene H; Silla, Josué M; Freitas, Matheus P

    2016-01-01

    An intriguing question in 3D-QSAR lies on which conformation(s) to use when generating molecular descriptors (MD) for correlation with bioactivity values. This is not a simple task because the bioactive conformation in molecule data sets is usually unknown and, therefore, optimized structures in a receptor-free environment are often used to generate the MD´s. In this case, a wrong conformational choice can cause misinterpretation of the QSAR model. The present computational work reports the conformational analysis of the volatile anesthetic isoflurane (2-chloro-2-(difluoromethoxy)-1,1,1-trifluoroethane) in the gas phase and also in polar and nonpolar implicit and explicit solvents to show that stable minima (ruled by intramolecular interactions) do not necessarily coincide with the bioconformation (ruled by enzyme induced fit). Consequently, a QSAR model based on two-dimensional chemical structures was built and exhibited satisfactory modeling/prediction capability and interpretability, then suggesting that these 2D MD´s can be advantageous over some three-dimensional descriptors. PMID:27340468

  12. 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.

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

    PubMed

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

    2015-11-17

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

  14. Quantitative structure-activity relationships (QSAR) for nitroaromatics to fathead minnow

    SciTech Connect

    Lang, P.Z.; Lu, G.H.; Ma, X.F.

    1994-12-31

    QSAR was studied with the reported semi-flow-through 96 h-LC{sub 50} values for fathead minnow using six physicochemical descriptors. The parameters used include {sup 1}X{sup v}, {sup 1}K{alpha}, {Sigma}{sigma}-, log P, I and E{sub LUMO}. The values of I were cited from literature (Hall et al., 1989). E{sub LUMO} was used instead of the reported E{sub 1/2}. E{sub LUMO} of 51 compounds was predicted by CNDO/2. By regression, a QSAR equation was obtained: {minus}log LC{sub 50} = 3.514 + 0.416 I + 0.344 {Sigma}{sigma}- (n = 35, r = 0.965, s = 0.194). The nitroaromatics include dinitrobenzenes, trinitrobenzenes, trinitrotoluenes and some substituted benzenes containing one or two -NO{sub 2} and one of -CH{sub 3}, -Cl, -F, -OH, -NH{sub 2} or -OCH{sub 3}. The QSAR equation was used to estimate LC{sub 50} for 16 nitroaromatics. Inferred from the obtained equation, of the compounds studied here, the dinitro compounds (their I and {Sigma}{sigma}- are all high) are reactive and the mononitro compounds are less reactive or non-reactive.

  15. More effective antimicrobial mastoparan derivatives, generated by 3D-QSAR-Almond and computational mutagenesis.

    PubMed

    Avram, Speranta; Buiu, Catalin; Borcan, Florin; Milac, Adina-Luminita

    2012-02-01

    Antimicrobial peptides are drugs used against a wide range of pathogens which present a great advantage: in contrast with antibiotics they do not develop resistance. The wide spectrum of antimicrobial peptides advertises them in the research and pharmaceutical industry as attractive starting points for obtaining new, more effective analogs. Here we predict the antimicrobial activity against Bacillus subtilis (expressed as minimal inhibitory concentration values) for 33 mastoparan analogs and their new derivatives by a non-aligned 3D-QSAR (quantitative structure-activity relationship) method. We establish the contribution to antimicrobial activity of molecular descriptors (hydrophobicity, hydrogen bond donor and steric), correlated with contributions from the membrane environment (sodium, potassium, chloride). Our best QSAR models show significant cross-validated correlation q(2) (0.55-0.75), fitted correlation r(2) (greater than 0.90) coefficients and standard error of prediction SDEP (less than 0.250). Moreover, based on our most accurate 3D-QSAR models, we propose nine new mastoparan analogs, obtained by computational mutagenesis, some of them predicted to have significantly improved antimicrobial activity compared to the parent compound.

  16. More effective dithiocarbamate derivatives inhibiting carbonic anhydrases, generated by QSAR and computational design.

    PubMed

    Avram, Speranta; Milac, Adina Luminita; Carta, Fabrizio; Supuran, Claudiu T

    2013-04-01

    Dithiocarbamates (DTC) are promising compounds with potential applications in antitumoral and glaucoma therapy. Our aim is to understand molecular features affecting DTC interaction with carbonic anhydrases (CAs), zinc-containing enzymes maintaining acid-base balance in blood and other tissues. To this end, we generate QSAR models based on a compound series containing 25 DTC, inhibitors of four human (h) CAs isoforms: hCA I, II, IX and XII. We establish that critical physicochemical parameters for DTC inhibitory activity are: hydrophobic, electronic, steric, topological and shape. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q(2) (0.55-0.73), fitted correlation r(2) (0.75-0.84) and standard error of prediction (0.47-0.23). Based on the established QSAR equations, we analyse 22 new DTC derivatives and identify DTC dicarboxilic acids derivatives and their esters as potentially improved inhibitors of CA I, II, IX and XII. PMID:23116520

  17. 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

  18. Is conformation a fundamental descriptor in QSAR? A case for halogenated anesthetics

    PubMed Central

    Guimarães, Maria C; Duarte, Mariene H; Silla, Josué M

    2016-01-01

    Summary An intriguing question in 3D-QSAR lies on which conformation(s) to use when generating molecular descriptors (MD) for correlation with bioactivity values. This is not a simple task because the bioactive conformation in molecule data sets is usually unknown and, therefore, optimized structures in a receptor-free environment are often used to generate the MD´s. In this case, a wrong conformational choice can cause misinterpretation of the QSAR model. The present computational work reports the conformational analysis of the volatile anesthetic isoflurane (2-chloro-2-(difluoromethoxy)-1,1,1-trifluoroethane) in the gas phase and also in polar and nonpolar implicit and explicit solvents to show that stable minima (ruled by intramolecular interactions) do not necessarily coincide with the bioconformation (ruled by enzyme induced fit). Consequently, a QSAR model based on two-dimensional chemical structures was built and exhibited satisfactory modeling/prediction capability and interpretability, then suggesting that these 2D MD´s can be advantageous over some three-dimensional descriptors. PMID:27340468

  19. Kernel-based partial least squares: application to fingerprint-based QSAR with model visualization.

    PubMed

    An, Yuling; Sherman, Woody; Dixon, Steven L

    2013-09-23

    Numerous regression-based and machine learning techniques are available for the development of linear and nonlinear QSAR models that can accurately predict biological endpoints. Such tools can be quite powerful in the hands of an experienced modeler, but too frequently a disconnect remains between the modeler and project chemist because the resulting QSAR models are effectively black boxes. As a result, learning methods that yield models that can be visualized in the context of chemical structures are in high demand. In this work, we combine direct kernel-based PLS with Canvas 2D fingerprints to arrive at predictive QSAR models that can be projected onto the atoms of a chemical structure, allowing immediate identification of favorable and unfavorable characteristics. The method is validated using binding affinities for ligands from 10 different protein targets covering 7 distinct protein families. Models with significant predictive ability (test set Q(2) > 0.5) are obtained for 6 of 10 data sets, and fingerprints are shown to consistently outperform large collections of classical physicochemical and topological descriptors. In addition, we demonstrate how a simple bootstrapping technique may be employed to obtain uncertainties that provide meaningful estimates of prediction accuracy.

  20. Development of QSARs for predicting the joint effects between cyanogenic toxicants and aldehydes.

    PubMed

    Lin, Zhifen; Yin, Kedong; Shi, Ping; Wang, Liansheng; Yu, Hongxia

    2003-10-01

    Quantitative structure-activity relationship (QSAR) approaches are proposed in this study to predict the joint effects of mixture toxicity. The initial investigation studies the joint effects between cyanogenic toxicants and aldehydes to Photobacterium phosphoreum. Joint effects are found to result from the formation of a carbanion intermediate produced through the chemical interactions between cyanogenic toxicants and aldehydes. Further research indicates that the formation of carbanion intermediate is highly correlated with not only the charge of the carbon atom in the -CHO of aldehydes but also the charge of the carbon atom (C) in the carbochain of cyanogenic toxicants. The charge of the carbon atom in the -CHO of aldehydes is quantified by using the Hammett constant (sigma(p)), and then, sigma(p)-based QSAR models are proposed to describe the relationships between the joint effects and the chemical structures of the aldehydes. By using the charge of carbon atom (C) in the carbochain of cyanogenic toxicants, another QSAR model is proposed to describe the relationship between the joint effects and the chemical structures of cyanogenic toxicants.

  1. Database Marketplace 2002: The Database Universe.

    ERIC Educational Resources Information Center

    Tenopir, Carol; Baker, Gayle; Robinson, William

    2002-01-01

    Reviews the database industry over the past year, including new companies and services, company closures, popular database formats, popular access methods, and changes in existing products and services. Lists 33 firms and their database services; 33 firms and their database products; and 61 company profiles. (LRW)

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

    PubMed Central

    2012-01-01

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

  3. A Large-Scale Empirical Evaluation of Cross-Validation and External Test Set Validation in (Q)SAR.

    PubMed

    Gütlein, Martin; Helma, Christoph; Karwath, Andreas; Kramer, Stefan

    2013-06-01

    (Q)SAR model validation is essential to ensure the quality of inferred models and to indicate future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to accept the (Q)SAR model, and to approve its use in real world scenarios as alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model, in particular whether to employ variants of cross-validation or external test set validation, is still under discussion. In this paper, we empirically compare a k-fold cross-validation with external test set validation. To this end we introduce a workflow allowing to realistically simulate the common problem setting of building predictive models for relatively small datasets. The workflow allows to apply the built and validated models on large amounts of unseen data, and to compare the performance of the different validation approaches. The experimental results indicate that cross-validation produces higher performant (Q)SAR models than external test set validation, reduces the variance of the results, while at the same time underestimates the performance on unseen compounds. The experimental results reported in this paper suggest that, contrary to current conception in the community, cross-validation may play a significant role in evaluating the predictivity of (Q)SAR models.

  4. 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

    PubMed

    Wongrattanakamon, Pathomwat; Lee, Vannajan Sanghiran; Nimmanpipug, Piyarat; Jiranusornkul, Supat

    2016-12-01

    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion. PMID:27626051

  5. 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity.

    PubMed

    Wongrattanakamon, Pathomwat; Lee, Vannajan Sanghiran; Nimmanpipug, Piyarat; Jiranusornkul, Supat

    2016-12-01

    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.

  6. Applying quantitative structure-activity relationship (QSAR) methodology for modeling postmortem redistribution of benzodiazepines and tricyclic antidepressants.

    PubMed

    Giaginis, Constantinos; Tsantili-Kakoulidou, Anna; Theocharis, Stamatios

    2014-06-01

    Postmortem redistribution (PMR) constitutes a multifaceted process, which complicates the interpretation of drug concentrations by forensic toxicologists. The present study aimed to apply quantitative structure-activity relationship (QSAR) analysis for modeling PMR data of structurally related drugs, 10 benzodiazepines and 10 tricyclic antidepressants. For benzodiazepines, an adequate QSAR model was obtained (R(2) = 0.98, Q(2) = 0.88, RMSEE = 0.12), in which energy, ionization and molecular size exerted significant impact. For tricyclic antidepressants, an adequate QSAR model with slightly inferior statistics (R(2) = 0.95, Q(2) = 0.87, RMSEE = 0.29) was established after exclusion of maprotiline, in which energy parameters, basicity character and lipophilicity exerted significant contribution. Thus, QSAR analysis could be used as a complementary tool to provide an informative illustration of the contributing molecular, physicochemical and structural properties in PMR process. However, the complexity, non-static and time-dependent nature of PMR endpoints raises serious concerns whether QSAR methodology could predict the degree of redistribution, highlighting the need for animal-derived PMR data.

  7. Trend Analyses of Nitrate in Danish Groundwater

    NASA Astrophysics Data System (ADS)

    Hansen, B.; Thorling, L.; Dalgaard, T.; Erlandsen, M.

    2012-04-01

    This presentation assesses the long-term development in the oxic groundwater nitrate concentration and nitrogen (N) loss due to intensive farming in Denmark. Firstly, up to 20-year time-series from the national groundwater monitoring network enable a statistically systematic analysis of distribution, trends and trend reversals in the groundwater nitrate concentration. Secondly, knowledge about the N surplus in Danish agriculture since 1950 is used as an indicator of the potential loss of N. Thirdly, groundwater recharge CFC (Chlorofluorocarbon) age determination allows linking of the first two dataset. The development in the nitrate concentration of oxic groundwater clearly mirrors the development in the national agricultural N surplus, and a corresponding trend reversal is found in groundwater. Regulation and technical improvements in the intensive farming in Denmark have succeeded in decreasing the N surplus by 40% since the mid 1980s while at the same time maintaining crop yields and increasing the animal production of especially pigs. Trend analyses prove that the youngest (0-15 years old) oxic groundwater shows more pronounced significant downward nitrate trends (44%) than the oldest (25-50 years old) oxic groundwater (9%). This amounts to clear evidence of the effect of reduced nitrate leaching on groundwater nitrate concentrations in Denmark. Are the Danish groundwater monitoring strategy obtimal for detection of nitrate trends? Will the nitrate concentrations in Danish groundwater continue to decrease or are the Danish nitrate concentration levels now appropriate according to the Water Framework Directive?

  8. The Danish Free School Tradition under Pressure

    ERIC Educational Resources Information Center

    Olsen, Tore Vincents

    2015-01-01

    The Danish free school tradition has entailed a large degree of associational freedom for non-governmental schools, religious as well as non-religious. Until the late 1990s, the non-governmental schools were under no strict ideological or pedagogical limitations; they could recruit teachers and students according to their own value base, and were…

  9. 3D QSAR investigations on locomotor activity of 5-cyano-N1,6-disubstituted 2-thiouracil derivatives.

    PubMed

    Kuchekar, B S; Pore, Y V

    2010-06-01

    Three dimensional quantitative structure activity relationship (3D QSAR) investigations were carried out on a series of 5-cyano-N1,6-disubstituted 2-thiouracil derivatives for their locomotor activity. The structures of all compounds were built on a workspace of VlifeMDS3.5 molecular modeling software and 3D QSAR models were generated by applying a partial least square (PLS) linear regression analysis coupled with a stepwise variable selection method. Both derived models were found to be statistically significant in terms of regression and internal and external predictive ability (r(2) = 0.9414 and 0.8511, q(2) = 0.8582 and 0.6222, pred_r(2) = 0.5142 and 0.7917). The QSAR models indicated that both electrostatic and steric interaction energies were contributing significantly to locomotor activity of thiouracil derivatives. PMID:22491179

  10. QSAR and Molecular Docking Studies of Oxadiazole-Ligated Pyrrole Derivatives as Enoyl-ACP (CoA) Reductase Inhibitors

    PubMed Central

    Asgaonkar, Kalyani D.; Mote, Ganesh D.; Chitre, Trupti S.

    2014-01-01

    A quantitative structure-activity relationship model was developed on a series of compounds containing oxadiazole-ligated pyrrole pharmacophore to identify key structural fragments required for anti-tubercular activity. Two-dimensional (2D) and three-dimensional (3D) QSAR studies were performed using multiple linear regression (MLR) analysis and k-nearest neighbour molecular field analysis (kNN-MFA), respectively. The developed QSAR models were found to be statistically significant with respect to training, cross-validation, and external validation. New chemical entities (NCEs) were designed based on the results of the 2D- and 3D-QSAR. NCEs were subjected to Lipinski’s screen to ensure the drug-like pharmacokinetic profile of the designed compounds in order to improve their bioavailability. Also, the binding ability of the NCEs with enoyl-ACP (CoA) reductase was assessed by docking. PMID:24634843

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

    NASA Astrophysics Data System (ADS)

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

    2013-11-01

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

  12. The air quality in Danish urban areas.

    PubMed

    Jensen, F P; Fenger, J

    1994-10-01

    The Danish air pollution abatement is based by and large on emission control. Since the ratification of the international sulfur protocol of 1985, there has been a continuous tightening of the permissible sulfur content in fuels and of the maximum emissions from power plants. As a consequence, the total annual emission of sulfur dioxide (SO2) has been reduced from 450,000 tons in the seventies to 180,000 tons in 1990. This has had a pronounced effect on the SO2 levels in Danish urban areas. Thus, in Copenhagen, the yearly averages have fallen to about 25%. For nitrogen oxides emitted from the power plants, similar regulations are in force. With this legislation, the most important and crucial source of air pollution in Danish urban areas is road traffic. The contribution of nitrogen oxides from national traffic accounts for nearly half the total Danish emission and is increasing steadily; this is consistent with an observed increase of nitrogen oxides in ambient air. The permissible levels of lead in petrol has been reduced drastically. After an introduction of reduced tax on lead-free petrol, it now accounts for more than two-thirds of the total consumption. As a result, the concentration of lead in urban ambient air has been reduced to less than one-sixth. The introduction of 3-way catalytic converters from October 1990 will result in reductions in the emission of a series of pollutants, e.g., lead, volatile organic compounds, carbon monoxide, and nitrogen oxides. In 1980, a Danish air quality monitoring program was established as a cooperative effort between the authorities, the Government, the countries, the municipalities, and the Greater Copenhagen Council.(ABSTRACT TRUNCATED AT 250 WORDS)

  13. The air quality in Danish urban areas.

    PubMed

    Jensen, F P; Fenger, J

    1994-10-01

    The Danish air pollution abatement is based by and large on emission control. Since the ratification of the international sulfur protocol of 1985, there has been a continuous tightening of the permissible sulfur content in fuels and of the maximum emissions from power plants. As a consequence, the total annual emission of sulfur dioxide (SO2) has been reduced from 450,000 tons in the seventies to 180,000 tons in 1990. This has had a pronounced effect on the SO2 levels in Danish urban areas. Thus, in Copenhagen, the yearly averages have fallen to about 25%. For nitrogen oxides emitted from the power plants, similar regulations are in force. With this legislation, the most important and crucial source of air pollution in Danish urban areas is road traffic. The contribution of nitrogen oxides from national traffic accounts for nearly half the total Danish emission and is increasing steadily; this is consistent with an observed increase of nitrogen oxides in ambient air. The permissible levels of lead in petrol has been reduced drastically. After an introduction of reduced tax on lead-free petrol, it now accounts for more than two-thirds of the total consumption. As a result, the concentration of lead in urban ambient air has been reduced to less than one-sixth. The introduction of 3-way catalytic converters from October 1990 will result in reductions in the emission of a series of pollutants, e.g., lead, volatile organic compounds, carbon monoxide, and nitrogen oxides. In 1980, a Danish air quality monitoring program was established as a cooperative effort between the authorities, the Government, the countries, the municipalities, and the Greater Copenhagen Council.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7821296

  14. The air quality in Danish urban areas.

    PubMed Central

    Jensen, F P; Fenger, J

    1994-01-01

    The Danish air pollution abatement is based by and large on emission control. Since the ratification of the international sulfur protocol of 1985, there has been a continuous tightening of the permissible sulfur content in fuels and of the maximum emissions from power plants. As a consequence, the total annual emission of sulfur dioxide (SO2) has been reduced from 450,000 tons in the seventies to 180,000 tons in 1990. This has had a pronounced effect on the SO2 levels in Danish urban areas. Thus, in Copenhagen, the yearly averages have fallen to about 25%. For nitrogen oxides emitted from the power plants, similar regulations are in force. With this legislation, the most important and crucial source of air pollution in Danish urban areas is road traffic. The contribution of nitrogen oxides from national traffic accounts for nearly half the total Danish emission and is increasing steadily; this is consistent with an observed increase of nitrogen oxides in ambient air. The permissible levels of lead in petrol has been reduced drastically. After an introduction of reduced tax on lead-free petrol, it now accounts for more than two-thirds of the total consumption. As a result, the concentration of lead in urban ambient air has been reduced to less than one-sixth. The introduction of 3-way catalytic converters from October 1990 will result in reductions in the emission of a series of pollutants, e.g., lead, volatile organic compounds, carbon monoxide, and nitrogen oxides. In 1980, a Danish air quality monitoring program was established as a cooperative effort between the authorities, the Government, the countries, the municipalities, and the Greater Copenhagen Council.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7821296

  15. FDA toxicity databases and real-time data entry

    SciTech Connect

    Arvidson, Kirk B.

    2008-11-15

    Structure-searchable electronic databases are valuable new tools that are assisting the FDA in its mission to promptly and efficiently review incoming submissions for regulatory approval of new food additives and food contact substances. The Center for Food Safety and Applied Nutrition's Office of Food Additive Safety (CFSAN/OFAS), in collaboration with Leadscope, Inc., is consolidating genetic toxicity data submitted in food additive petitions from the 1960s to the present day. The Center for Drug Evaluation and Research, Office of Pharmaceutical Science's Informatics and Computational Safety Analysis Staff (CDER/OPS/ICSAS) is separately gathering similar information from their submissions. Presently, these data are distributed in various locations such as paper files, microfiche, and non-standardized toxicology memoranda. The organization of the data into a consistent, searchable format will reduce paperwork, expedite the toxicology review process, and provide valuable information to industry that is currently available only to the FDA. Furthermore, by combining chemical structures with genetic toxicity information, biologically active moieties can be identified and used to develop quantitative structure-activity relationship (QSAR) modeling and testing guidelines. Additionally, chemicals devoid of toxicity data can be compared to known structures, allowing for improved safety review through the identification and analysis of structural analogs. Four database frameworks have been created: bacterial mutagenesis, in vitro chromosome aberration, in vitro mammalian mutagenesis, and in vivo micronucleus. Controlled vocabularies for these databases have been established. The four separate genetic toxicity databases are compiled into a single, structurally-searchable database for easy accessibility of the toxicity information. Beyond the genetic toxicity databases described here, additional databases for subchronic, chronic, and teratogenicity studies have been prepared.

  16. FDA toxicity databases and real-time data entry.

    PubMed

    Arvidson, Kirk B

    2008-11-15

    Structure-searchable electronic databases are valuable new tools that are assisting the FDA in its mission to promptly and efficiently review incoming submissions for regulatory approval of new food additives and food contact substances. The Center for Food Safety and Applied Nutrition's Office of Food Additive Safety (CFSAN/OFAS), in collaboration with Leadscope, Inc., is consolidating genetic toxicity data submitted in food additive petitions from the 1960s to the present day. The Center for Drug Evaluation and Research, Office of Pharmaceutical Science's Informatics and Computational Safety Analysis Staff (CDER/OPS/ICSAS) is separately gathering similar information from their submissions. Presently, these data are distributed in various locations such as paper files, microfiche, and non-standardized toxicology memoranda. The organization of the data into a consistent, searchable format will reduce paperwork, expedite the toxicology review process, and provide valuable information to industry that is currently available only to the FDA. Furthermore, by combining chemical structures with genetic toxicity information, biologically active moieties can be identified and used to develop quantitative structure-activity relationship (QSAR) modeling and testing guidelines. Additionally, chemicals devoid of toxicity data can be compared to known structures, allowing for improved safety review through the identification and analysis of structural analogs. Four database frameworks have been created: bacterial mutagenesis, in vitro chromosome aberration, in vitro mammalian mutagenesis, and in vivo micronucleus. Controlled vocabularies for these databases have been established. The four separate genetic toxicity databases are compiled into a single, structurally-searchable database for easy accessibility of the toxicity information. Beyond the genetic toxicity databases described here, additional databases for subchronic, chronic, and teratogenicity studies have been prepared.

  17. Existing data sources for clinical epidemiology: the Danish National Pathology Registry and Data Bank

    PubMed Central

    Erichsen, Rune; Lash, Timothy L; Hamilton-Dutoit, Stephen J; Bjerregaard, Beth; Vyberg, Mogens; Pedersen, Lars

    2010-01-01

    Diagnostic histological and cytological specimens are routinely stored in pathology department archives. These biobanks are a valuable research resource for many diseases, particularly if they can be linked to high quality population-based health registries, allowing large retrospective epidemiological studies to be carried out. Such studies are of significant importance, for example in the search for novel prognostic and predictive biomarkers in the era of personalized medicine. Denmark has a wealth of highly-regarded population-based registries that are ideally suited to conduct this type of epidemiological research. We describe two recent additions to these databases: the Danish National Pathology Registry (DNPR) and its underlying national online registration database, the Danish Pathology Data Bank (DPDB). The DNPR and the DPDB contain detailed nationwide records of all pathology specimens analyzed in Denmark since 1997, and an incomplete but nonetheless valuable record of specimens from some pathology departments dating back to the 1970s. The data are of high quality and completeness and are sufficient to allow precise and efficient localization of the specimens. We describe the relatively uncomplicated procedures required to use these pathology databases in clinical research and to gain access to the archived specimens. PMID:20865103

  18. 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

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

    PubMed Central

    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. PMID:25317190

  20. Overlap in Bibliographic Databases.

    ERIC Educational Resources Information Center

    Hood, William W.; Wilson, Concepcion S.

    2003-01-01

    Examines the topic of Fuzzy Set Theory to determine the overlap of coverage in bibliographic databases. Highlights include examples of comparisons of database coverage; frequency distribution of the degree of overlap; records with maximum overlap; records unique to one database; intra-database duplicates; and overlap in the top ten databases.…

  1. Annual incidence, prevalence and transmission characteristics of Streptococcus agalactiae in Danish dairy herds.

    PubMed

    Mweu, Marshal M; Nielsen, Søren S; Halasa, Tariq; Toft, Nils

    2012-10-01

    Contagious mastitis pathogens continue to pose an economic threat to the dairy industry. An understanding of their frequency and transmission dynamics is central to evaluating the effectiveness of control programmes. The objectives of this study were twofold: (1) to estimate the annual herd-level incidence rates and apparent prevalences of Streptococcus agalactiae (S. agalactiae) in the population of Danish dairy cattle herds over a 10-year period from 2000 to 2009 inclusive and (2) to estimate the herd-level entry and exit rates (demographic parameters), the transmission parameter, β, and recovery rate for S. agalactiae infection. Data covering the specified period, on bacteriological culture of all bulk tank milk samples collected annually as part of the mandatory Danish S. agalactiae surveillance scheme, were extracted from the Danish Cattle Database and subsequently analysed. There was an increasing trend in both the incidence and prevalence of S. agalactiae over the study period. Per 100 herd-years the value of β was 54.1 (95% confidence interval [CI] 46.0-63.7); entry rate 0.3 (95% CI 0.2-0.4); infection-related exit rate 7.1 (95% CI 5.6-8.9); non-infection related exit rate 9.2 (95% CI 7.4-11.5) and recovery rate 40.0 (95% CI 36.8-43.5). This study demonstrates a need to tighten the current controls against S. agalactiae in order to lower its incidence. PMID:22560559

  2. Development of QSARs for the toxicity of chlorobenzenes to the soil dwelling springtail Folsomia candida.

    PubMed

    Giesen, Daniel; Jonker, Michiel T O; van Gestel, Cornelis A M

    2012-05-01

    To meet the goals of Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) as formulated by the European Commission, fast and resource-effective tools are needed to predict the toxicity of compounds in the environment. We developed quantitative structure-activity relationships (QSARs) for the toxicity of nine chlorinated benzenes to the soil-dwelling collembolan Folsomia candida in natural LUFA2.2 (Landwirtschaftliche Untersuchungs und Forschungsanstalt [LUFA]) standard soil and in Organisation for Economic Co-operation and Development artificial soil. Toxicity endpoints used were the effect concentrations causing 10% (EC10) and 50% (EC50) reduction in the reproduction of the test organism over 28 d, while lethal effects on survival (LC50) were used for comparisons with earlier studies. Chlorobenzene toxicity was based on concentrations in interstitial water as estimated using nominal concentrations in soil and literature soil-water partition coefficients. Additionally, for LUFA2.2 soil the estimated concentrations in interstitial water were experimentally determined by solid-phase microextraction measurements. Measured and estimated concentrations showed the same general trend, but significant differences were observed. With the exception of hexachlorobenzene, estimated EC10 and EC50 values were all negatively correlated with their logK(ow) and QSARs were developed. However, no correlation for the LC50 could be derived and 1,2,4,5-tetrachlorobenzene and hexachlorobenzene had no effect on adult survival at all. The derived QSARs may contribute to the development of better ecotoxicity-based models serving the REACH program. PMID:22431168

  3. Comparative QSAR analyses of competitive CYP2C9 inhibitors using three-dimensional molecular descriptors.

    PubMed

    Lather, Viney; Fernandes, Miguel X

    2011-07-01

    One of the biggest challenges in QSAR studies using three-dimensional descriptors is to generate the bioactive conformation of the molecules. Comparative QSAR analyses have been performed on a dataset of 34 structurally diverse and competitive CYP2C9 inhibitors by generating their lowest energy conformers as well as additional multiple conformers for the calculation of molecular descriptors. Three-dimensional descriptors accounting for the spatial characteristics of the molecules calculated using E-Dragon were used as the independent variables. The robustness and the predictive performance of the developed models were verified using both the internal [leave-one-out (LOO)] and external statistical validation (test set of 12 inhibitors). The best models (MLR using GETAWAY descriptors and partial least squares using 3D-MoRSE) were obtained by using the multiple conformers for the calculation of descriptors and were selected based upon the higher external prediction ( values of 0.65 and 0.63, respectively) and lower root mean square error of prediction (0.48 and 0.48, respectively). The predictive ability of the best model, i.e., MLR using GETAWAY descriptors was additionally verified on an external test set of quinoline-4-carboxamide analogs and resulted in an value of 0.6. These simple and alignment-independent QSAR models offer the possibility to predict CYP2C9 inhibitory activity of chemically diverse ligands in the absence of X-ray crystallographic information of target protein structure and can provide useful insights about the ADMET properties of candidate molecules in the early phases of drug discovery.

  4. QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors.

    PubMed

    Fatemi, Mohammad H; Heidari, Afsane; Gharaghani, Sajjad

    2015-03-21

    In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

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

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2015-04-01

    Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). PMID:25465947

  6. QSAR modeling and molecular interaction analysis of natural compounds as potent neuraminidase inhibitors.

    PubMed

    Sun, Jiaying; Mei, Hu

    2016-04-26

    Different QSAR models of 40 natural compounds as neuraminidase inhibitors (NIs) are developed to comprehend chemical-biological interactions and predict activities against neuraminidase (NA) from Clostridium perfringens. Based on the constitutional, topological and conformational descriptors, R(2) and Q(2) values of the obtained SRA model are 0.931 and 0.856. The R(2) and Q(2) values of the constructed HQSAR and almond models are 0.903 and 0.767, 0.904 and 0.511, respectively. Based on the pharmacophore alignment, R(2) and Q(2) values of the optimal CoMSIA model are 0.936 and 0.654. Moreover, Rtest(2) and Qext(2) of values of SRA, HQSAR, almond and CoMSIA models are 0.611 and 0.565, 0.753 and 0.750, 0.612 and 0.582, 0.582 and 0.571, respectively. So, QSAR models have good predictive capability. They can be further used to evaluate and screen new compounds. Moreover, hydrogen bonds and electrostatic factors have high contributions to activities. To understand molecular interactions between natural compounds and NA from Clostridium perfringens, molecular docking is investigated. The docking results elucidate that Arg266, Asp291, Asp328, Tyr485, Glu493, Arg555, Arg615 and Tyr655 are especially the key residues in the active site of 2bf6. Hydrogen bonds and electrostatics are key factors, which impact the interactions between NIs and NA. So, the influential factors of interactions between NIs and NA in the docking results are in agreement with the QSAR results. PMID:27008437

  7. 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

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

    PubMed

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

    2015-11-17

    The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes. PMID:26451961

  9. A quantitative structure activity relationships (QSAR) analysis of triarylmethane dye tracers

    NASA Astrophysics Data System (ADS)

    Mon, Jarai; Flury, Markus; Harsh, James B.

    2006-01-01

    Dyes are important hydrological tracers. Many different dyes have been proposed as optimal tracers, but none of these dyes can be considered an ideal water tracer. Some dyes are toxic and most sorb to subsurface materials. The objective of this study was to find the molecular structure of an optimal water tracer. We used QSAR to screen a large number of hypothetical molecules, belonging to the class of triarylmethane dyes, in regard to their sorption characteristics to a sandy soil. The QSAR model was based on experimental sorption data obtained from four triarylmethane dyes: C.I. Food Blue 2 (C.I. 42090; Brilliant Blue FCF), C.I. Food Green 3 (C.I. 42053; FD&C Green No. 3), C.I. Acid Blue 7 (C.I. 42080; ORCOacid Blue A 150%), and C.I. Acid Green 9 (C.I. 42100; ORCOacid Fast Green B). Sorption characteristics of the dyes to the sandy soil were expressed with the Langmuir isotherm. Our premise was that dye sorption can be reduced by attachment of sulfonic acid (SO 3) groups to the triarylmethane template. About 70 hypothetical dyes were created and QSAR were used to estimate sorption characteristics. The results indicated that both the position and the number of SO 3 groups affected dye sorption. Sorption decreased with increasing number of SO 3 groups attached to the molecule. Increasing the number of sulfonic acid groups also decreases the toxicity of the compounds. An optimal triarylmethane water tracer contains 4 to 6 SO 3 groups.

  10. Synthesis, antimalarial properties and 2D-QSAR studies of novel triazole-quinine conjugates.

    PubMed

    Faidallah, Hassan M; Panda, Siva S; Serrano, Juan C; Girgis, Adel S; Khan, Khalid A; Alamry, Khalid A; Therathanakorn, Tanya; Meyers, Marvin J; Sverdrup, Francis M; Eickhoff, Christopher S; Getchell, Stephen G; Katritzky, Alan R

    2016-08-15

    Click chemistry technique led to novel 1,2,3-triazole-quinine conjugates 8a-g, 10a-o, 11a-h and 13 utilizing benzotriazole-mediated synthetic approach with excellent yields. Some of the synthesized analogs (11a, 11d-h) exhibited antimalarial properties against Plasmodium falciparum strain 3D7 with potency higher than that of quinine (standard reference used) through in vitro standard procedure bio-assay. Statistically significant BMLR-QSAR model describes the bio-properties, validates the observed biological observations and identifies the most important parameters governing bio-activity. PMID:27298002

  11. Prediction of aromatic amine carcinogenicity: QSAR base on calculated delocalizibility of hypothetical nitrenium ion intermediate

    SciTech Connect

    Purdy, R.

    1995-12-31

    Predictors for the reactivity of primary aromatic amines were hypothesized and tested on a small set of amines. It was found that the delocalizibility on the nitrogen of the previously hypothesized nitrenium ion intermediate was the only good predictor. The strength of this predictor was tested on a larger set of amines and a cut off value for discriminating between carcinogens and noncarcinogens was chosen. This QSAR supports the hypothesis that a nitrenium ion is an intermediate in the activation of primary aromatic amines to active carcinogens.

  12. Antioxidant activity of flavonoids: a QSAR modeling using Fukui indices descriptors.

    PubMed

    Djeradi, Houria; Rahmouni, Ali; Cheriti, Abdelkrim

    2014-10-01

    A QSAR model to predict the antioxidant activity of flavonoid compounds was developed. New electronic structure descriptors which are Fukui indices are correlated to the radical scavenging of flavonoids. These indices are obtained at DFT/B3LYP level of chemical quantum theory. The logIC50 experimental values of antioxidant activity are taken from the literature. The model is based on the multilinear regression method. Both experimental and calculated data of 36 flavonoids compounds were analyzed. A good correlation coefficient (R(2) = 0.8159) is obtained and the antioxidant activities of test compounds are well predicted. PMID:25311723

  13. 3D-QSAR Study of 7,8-Dialkyl-1,3-diaminopyrrolo-[3,2-f] Quinazolines with Anticancer Activity as DHFR Inhibitors

    NASA Astrophysics Data System (ADS)

    Chen, Jin-can; Chen, Lan-mei; Liao, Si-yan; Qian, Li; Zheng, Kang-cheng

    2009-06-01

    A three-dimensional quantitative structure-activity relationship (3D-QSAR) study of a series of 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] quinazolines with anticancer activity as dihydrofolate reductase (DHFR) inhibitors was carried out by using the comparative molecular field analysis (CoMFA), on the basis of our reported 2D-QSAR of these compounds. The established 3D-QSAR model has good quality of statistics and good prediction ability; the non cross-validation correlation coefficient and the cross-validation value of this model are 0.993 and 0.619, respectively, the F value is 193.4, and the standard deviation SD is 0.208. This model indicates that the steric field factor plays a much more important role than the electrostatic one, in satisfying agreement with the published 2D-QSAR model. However, the 3D-QSAR model offers visual images of the steric field and the electrostatic field. The 3D-QSAR study further suggests the following: to improve the activity, the substituent R' should be selected to be a group with an adaptive bulk like Et or i-Pr, and the substituent R should be selected to be a larger alkyl. In particular, based on our present 3D-QSAR as well as the published 2D-QSAR, the experimentally-proposed hydrophobic binding mechanism on the receptor-binding site of the DHFR can be further explained in theory. Therefore, the QSAR studies help to further understand the “hydrophobic binding" action mechanism of this kind of compounds, and to direct the molecular design of new drugs with higher activity.

  14. The eNanoMapper database for nanomaterial safety information

    PubMed Central

    Chomenidis, Charalampos; Doganis, Philip; Fadeel, Bengt; Grafström, Roland; Hardy, Barry; Hastings, Janna; Hegi, Markus; Jeliazkov, Vedrin; Kochev, Nikolay; Kohonen, Pekka; Munteanu, Cristian R; Sarimveis, Haralambos; Smeets, Bart; Sopasakis, Pantelis; Tsiliki, Georgia; Vorgrimmler, David; Willighagen, Egon

    2015-01-01

    Summary Background: The NanoSafety Cluster, a cluster of projects funded by the European Commision, identified the need for a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs). Ontologies, open standards, and interoperable designs were envisioned to empower a harmonized approach to European research in nanotechnology. This setting provides a number of opportunities and challenges in the representation of nanomaterials data and the integration of ENM information originating from diverse systems. Within this cluster, eNanoMapper works towards supporting the collaborative safety assessment for ENMs by creating a modular and extensible infrastructure for data sharing, data analysis, and building computational toxicology models for ENMs. Results: The eNanoMapper database solution builds on the previous experience of the consortium partners in supporting diverse data through flexible data storage, open source components and web services. We have recently described the design of the eNanoMapper prototype database along with a summary of challenges in the representation of ENM data and an extensive review of existing nano-related data models, databases, and nanomaterials-related entries in chemical and toxicogenomic databases. This paper continues with a focus on the database functionality exposed through its application programming interface (API), and its use in visualisation and modelling. Considering the preferred community practice of using spreadsheet templates, we developed a configurable spreadsheet parser facilitating user friendly data preparation and data upload. We further present a web application able to retrieve the experimental data via the API and analyze it with multiple data preprocessing and machine learning algorithms. Conclusion: We demonstrate how the eNanoMapper database is used to import and publish online ENM and assay data from several data sources, how the “representational state transfer

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

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity. PMID:25510831

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

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

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

  17. Cancer incidence among Danish stone workers.

    PubMed

    Guénel, P; Højberg, G; Lynge, E

    1989-08-01

    The lung cancer incidence of 2071 Danish stone workers was followed for a 42-year period. The expected numbers of cancer cases were based on the incidence rates for all Danish men after adjustment for region, and the data were analyzed separately for skilled and unskilled stone workers. The standardized incidence ratio (SIR) for lung cancer was 200 (44 observed, 22.0 expected) for all skilled stone workers, 808 (7 observed, 0.9 expected) for skilled sandstone cutters in Copenhagen, 119 (8 observed, 6.5 expected) for skilled granite cutters in Bornholm, 181 (24 observed, 13.2 expected) for all unskilled stone workers, 246 (17 observed, 6.9 expected) for unskilled workers in the road and building material industry, and 111 (7 observed, 6.3 expected) for unskilled workers in the stonecutting industry. Smoking was unlikely alone to explain the excess risk, and the available data on levels of exposure in the Danish stone industry point to a possible dose-response relationship between exposure to respirable silica dust and the incidence of lung cancer.

  18. Validation of the danish national diabetes register.

    PubMed

    Green, Anders; Sortsø, Camilla; Jensen, Peter Bjødstrup; Emneus, Martha

    2015-01-01

    The Danish National Diabetes Register (NDR) was established in 2006 and builds on data from Danish health registers. We validated the content of NDR, using full information from the Danish National Patient Register and data from the literature. Our study indicates that the completeness in NDR is ≥95% concerning ascertainment from data sources specific for diabetes, ie, prescriptions with antidiabetic drugs and diagnoses of diabetes in the National Patient Register. Since the NDR algorithm ignores diabetes-related hospital contacts terminated before 1990, the establishment of the date of inclusion is systematically delayed for ≥10% of the registrants in general and for ≥30% of the inclusions before 1997 in particular. This bias is enhanced for ascertainment by chiropody services and by frequent measurements of blood glucose because the date of reimbursement of services, rather than the date of encounter, has been taken as the date of inclusion in NDR. We also find that some 20% of the registrations in NDR may represent false positive inclusions of persons with frequent measurements of blood glucose without having diabetes. We conclude that NDR is a novel initiative to support research in the epidemiological and public health aspects of diabetes in Denmark, but we also present a list of recommended changes for improving validity, by reducing the impact of current sources of bias and misclassifications.

  19. The influence of data curation on QSAR Modeling - examining issues of quality versus quantity of data (American Chemical Society)

    EPA Science Inventory

    This presentation will examine the impact of data quality on the construction of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. We have developed a public-facing platform to provide access to predictive models. As part of the work we ha...

  20. The influence of data curation on QSAR Modeling – examining issues of quality versus quantity of data (SOT)

    EPA Science Inventory

    The construction of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available ...

  1. INFLUENCE OF MATRIX FORMULATION ON DERMAL PERCUTANEOUS ABSORPTION OF TRIAZOLE FUNGICIDES USING QSAR AND PBPK / PD MODELS

    EPA Science Inventory

    The objective of this work is to use the Exposure Related Dose Estimating Model (ERDEM) and quantitative structure-activity relationship (QSAR) models to develop an assessment tool for human exposure assessment to triazole fungicides. A dermal exposure route is used for the physi...

  2. 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.

  3. 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.

  4. Pharmacophore modeling, molecular docking, QSAR, and in silico ADMET studies of gallic acid derivatives for immunomodulatory activity.

    PubMed

    Yadav, Dharmendra Kumar; Khan, Feroz; Negi, Arvind Singh

    2012-06-01

    Immunomodulation refers to an alteration in the immune response due to the intrusion of foreign molecules into the body. In the present communication, QSAR and docking studies of gallic acid derivatives were performed in relation to their immunomodulatory activities. Screening through the use of a QSAR model suggested that the compounds G-4, G-7, G-9, G-10, G-12, and G-13 possess immunomodulatory activity. Activity was predicted using a statistical model developed by the forward stepwise multiple linear regression method. The correlation coefficient (r(2)) and the prediction accuracy (rCV(2)) of the QSAR model were 0.99 and 0.96, respectively. The QSAR study indicated that chemical descriptors-dipole moment, steric energy, amide group count, λ(max) (UV-visible) and molar refractivity-are well correlated with activity, while decreases in the dipole moment, steric energy, and molar refractivity were negatively correlated. A molecular docking study showed that the compounds had high binding affinities for the INFα-2, IL-6, and IL-4 receptors. Binding site residues formed H-bonds with the designed gallic acid derivatives G-3, G-4, G-5, G-6, G-7, and G-10. Moreover, based on screening for oral bioavailability, in silico ADME, and toxicity risk assessment, we concluded that compound G-7 exhibits marked immunomodulatory activity, comparable to levamisole.

  5. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis.

    PubMed

    Polishchuk, Pavel; Tinkov, Oleg; Khristova, Tatiana; Ognichenko, Ludmila; Kosinskaya, Anna; Varnek, Alexandre; Kuz'min, Victor

    2016-08-22

    This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms_qsar.php . PMID:27419846

  6. Comparative QSAR Analysis of 3,5-bis (Arylidene)-4-Piperidone Derivatives: the Development of Predictive Cytotoxicity Models

    PubMed Central

    Edraki, Najmeh; Das, Umashankar; Hemateenejad, Bahram; Dimmock, Jonathan R.; Miri, Ramin

    2016-01-01

    1-[4-(2-Alkylaminoethoxy) phenylcarbonyl]-3,5-bis(arylidene)-4-piperidones are a novel class of potent cytotoxic agents. These compounds demonstrate low micromolar to submicromolar IC50 values against human Molt 4/C8 and CEM T-lymphocytes and murine leukemia L1210 cells. In this study, a comparative QSAR investigation was performed on a series of 3,5-bis (arylidene)-4-piperidones using different chemometric tools to develop the best predictive models for further development of analogs with improved cytotoxicity. All the QSAR models were validated by internal validation tests. The QSAR models obtained by GA-PLS method were considered the best as compared to MLR method. The best QSAR model obtained by GA-PLS analysis on L1210, CEM and Molt4/C8 demonstrated good predictively with R2pred values ranging from 0.94-0.80. Molecular density, topological (X2A) and geometrical indices of the molecules were found to be the most important factors for determining cytotoxic properties. PMID:27642313

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

    PubMed

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

    2012-08-01

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

  8. Synthesis, evaluation and QSAR studies of 16-(4 & 3,4-substituted) benzylidene androstene derivatives as anticancer agents.

    PubMed

    Dubey, S; Kaur, P; Jindal, D P; Satyanarayan, Y D; Piplani, P

    2008-05-01

    In a systematic effort aimed at identifying new steroidal cytotoxic agents with potent antipoliferative activity against cancer cells and developing their QSAR models, series of 4-nitro, 4-isopropyl, 4-methoxy and 3,4-dimethoxy substituted benzylidene androst-5-ene derivatives were synthesized. The selected compounds were evaluated for antineoplastic activity against a panel of three human cell lines-breast, CNS and lungs at NCI, Bethesda, USA. The results presented herein reports that compounds 7, 9, 10, 15,16, 18, 20-25, 30, 32-36 and 44 have been found to be active anticancer agents. The QSAR of 20 compounds was performed separately for each cell line and best-fit QSAR models are developed. The QSAR models obtained have shown significant correlations (r(2) range: 0.9163 to 0.8164) and good predictive performance (q(2) range: 0.8499- 0.6320). The validation of models has also been performed using the test set of compounds 5, 15 and 44.

  9. An examination of data quality on QSAR Modeling in regards to the environmental sciences (UNC-CH talk)

    EPA Science Inventory

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to...

  10. Comparative QSAR Analysis of 3,5-bis (Arylidene)-4-Piperidone Derivatives: the Development of Predictive Cytotoxicity Models.

    PubMed

    Edraki, Najmeh; Das, Umashankar; Hemateenejad, Bahram; Dimmock, Jonathan R; Miri, Ramin

    2016-01-01

    1-[4-(2-Alkylaminoethoxy) phenylcarbonyl]-3,5-bis(arylidene)-4-piperidones are a novel class of potent cytotoxic agents. These compounds demonstrate low micromolar to submicromolar IC50 values against human Molt 4/C8 and CEM T-lymphocytes and murine leukemia L1210 cells. In this study, a comparative QSAR investigation was performed on a series of 3,5-bis (arylidene)-4-piperidones using different chemometric tools to develop the best predictive models for further development of analogs with improved cytotoxicity. All the QSAR models were validated by internal validation tests. The QSAR models obtained by GA-PLS method were considered the best as compared to MLR method. The best QSAR model obtained by GA-PLS analysis on L1210, CEM and Molt4/C8 demonstrated good predictively with R(2) pred values ranging from 0.94-0.80. Molecular density, topological (X2A) and geometrical indices of the molecules were found to be the most important factors for determining cytotoxic properties. PMID:27642313

  11. In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors.

    PubMed

    Asadollahi-Baboli, M

    2016-08-01

    Mutated epidermal growth factor receptor (EGFR-T790M) inhibitors hold promise as new agents against cancer. Molecular docking and QSAR analysis were performed based on a series of fifty-three quinazoline derivatives to elucidate key structural and physicochemical properties affecting inhibitory activity. Molecular docking analysis identified the true conformations of ligands in the receptor's active pocket. The structural features of the ligands, expressed as molecular descriptors, were derived from the obtained docked conformations. Non-linear and spline QSAR models were developed through novel genetic algorithm and artificial neural network (GA-ANN) and multivariate adaptive regression spline techniques, respectively. The former technique was employed to consider non-linear relation between molecular descriptors and inhibitory activity of quinazoline derivatives. The later technique was also used to describe the non-linearity using basis functions and sub-region equations for each descriptor. Our QSAR model gave a high predictive performance [Formula: see text] and [Formula: see text]) using diverse validation techniques. Eight new compounds were designed using our QSAR model as potent EGFR-T790M inhibitors. Overall, the proposed in silico strategy based on docked derived descriptor and non-linear descriptor subset selection may help design novel quinazoline derivatives with improved EGFR-T790M inhibitory activity. PMID:27209475

  12. 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...

  13. Receptor-independent 4D-QSAR analysis of a set of norstatine derived inhibitors of HIV-1 protease.

    PubMed

    Senese, Craig L; Hopfinger, A J

    2003-01-01

    A training set of 27 norstatine derived inhibitors of HIV-1 protease, based on the 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acid core (AHPBA), for which the -log IC(50) values were measured, was used to construct 4D-QSAR models. Five unique RI-4D-QSAR models, from two different alignments, were identified (q(2) = 0.86-0.95). These five models were used to map the atom type morphology of the lining of the inhibitor binding site at the HIV protease receptor site as well as predict the inhibition potencies of seven test set compounds for model validation. The five models, overall, predict the -log IC(50) activity values for the test set compounds in a manner consistent with their q(2) values. The models also correctly identify the hydrophobic nature of the HIV protease receptor site, and inferences are made as to further structural modifications to improve the potency of the AHPBA inhibitors of HIV protease. The finding of five unique, and nearly statistically equivalent, RI-4D-QSAR models for the training set demonstrates that there can be more than one way to fit structure-activity data even within a given QSAR methodology. This set of unique, equally good individual models is referred to as the manifold model.

  14. Databases: Beyond the Basics.

    ERIC Educational Resources Information Center

    Whittaker, Robert

    This presented paper offers an elementary description of database characteristics and then provides a survey of databases that may be useful to the teacher and researcher in Slavic and East European languages and literatures. The survey focuses on commercial databases that are available, usable, and needed. Individual databases discussed include:…

  15. Reflective Database Access Control

    ERIC Educational Resources Information Center

    Olson, Lars E.

    2009-01-01

    "Reflective Database Access Control" (RDBAC) is a model in which a database privilege is expressed as a database query itself, rather than as a static privilege contained in an access control list. RDBAC aids the management of database access controls by improving the expressiveness of policies. However, such policies introduce new interactions…

  16. Comparison of Performance of Docking, LIE, Metadynamics and QSAR in Predicting Binding Affinity of Benzenesulfonamides.

    PubMed

    Raškevičius, Vytautas; Kairys, Visvaldas

    2015-01-01

    The design of inhibitors specific for one relevant carbonic anhydrase isozyme is the major challenge in the new therapeutic agents development. Comparative computational chemical structure and biological activity relationship studies on a series of carbonic anhydrase II inhibitors, benzenesulfonamide derivatives, bearing pyrimidine moieties are reported in this paper using docking, Linear Interaction Energy (LIE), Metadynamics and Quantitative Structure Activity Relationship (QSAR) methods. The computed binding affinities were compared with the experimental data with the goal to explore strengths and weaknesses of various approaches applied to the investigated carbonic anhydrase/inhibitor system. From the tested methods initially only QSAR showed promising results (R2=0.83-0.89 between experimentally determined versus predicted pKd values.). Possible reasons for this performance were discussed. A modification of the LIE method was suggested which used an alternative LIE-like equation yielding significantly improved results (R2 between the experimentally determined versus the predicted ΔG(bind) improved from 0.24 to 0.50).

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    1998-01-01

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

  19. 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.

  20. 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.

  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. Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols

    PubMed Central

    Mitra, Indrani; Saha, Achintya; Roy, Kunal

    2011-01-01

    An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico. PMID:21617771

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

    PubMed

    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 (q(2)) 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

  4. Peptide reactivity associated with skin sensitization: The QSAR Toolbox and TIMES compared to the DPRA.

    PubMed

    Urbisch, D; Honarvar, N; Kolle, S N; Mehling, A; Ramirez, T; Teubner, W; Landsiedel, R

    2016-08-01

    The molecular initiating event (MIE) of skin sensitization is the binding of a hapten to dermal proteins. This can be assessed using the in chemico direct peptide reactivity assay (DPRA) or in silico tools such as the QSAR Toolbox and TIMES SS. In this study, the suitability of these methods was analyzed by comparing their results to in vivo sensitization data of LLNA and human studies. Compared to human data, 84% of non-sensitizers and sensitizers yielded consistent results in the DPRA. In silico tools resulted in 'no alert' for 83%-100% of the non-sensitizers, but alerted only 55%-61% of the sensitizers. The inclusion of biotic and abiotic transformation simulations yielded more alerts for sensitizers, but simultaneously dropped the number of non-alerted non-sensitizers. In contrast to the DPRA, in silico tools were more consistent with results of the LLNA than human data. Interestingly, the new "DPRA profilers" (QSAR Toolbox) provided unsatisfactory results. Additionally, the results were combined in the '2 out of 3' prediction model with in vitro data derived from LuSens and h-CLAT. Using DPRA results, the model identified 90% of human sensitizers and non-sensitizers; using in silico results (including abiotic and biotic activations) instead of DPRA results led to a comparable high predictivity. PMID:27090964

  5. 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.

  6. 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

  7. Synthesis, biological activities, and quantitative structure-activity relationship (QSAR) study of novel camptothecin analogues.

    PubMed

    Wu, Dan; Zhang, Shao-Yong; Liu, Ying-Qian; Wu, Xiao-Bing; Zhu, Gao-Xiang; Zhang, Yan; Wei, Wei; Liu, Huan-Xiang; Chen, An-Liang

    2015-05-13

    In continuation of our program aimed at the development of natural product-based pesticidal agents, three series of novel camptothecin derivatives were designed, synthesized, and evaluated for their biological activities against T. Cinnabarinus, B. brassicae, and B. xylophilus. All of the derivatives showed good-to-excellent activity against three insect species tested, with LC50 values ranging from 0.00761 to 0.35496 mmol/L. Remarkably, all of the compounds were more potent than CPT against T. Cinnabarinus, and compounds 4d and 4c displayed superior activity (LC50 0.00761 mmol/L and 0.00942 mmol/L, respectively) compared with CPT (LC50 0.19719 mmol/L) against T. Cinnabarinus. Based on the observed bioactivities, preliminary structure-activity relationship (SAR) correlations were also discussed. Furthermore, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) was built. The model gave statistically significant results with the cross-validated q2 values of 0.580 and correlation coefficient r2 of 0.991 and  of 0.993. The QSAR analysis indicated that the size of the substituents play an important in the activity of 7-modified camptothecin derivatives. These findings will pave the way for further design, structural optimization, and development of camptothecin-derived compounds as pesticidal agents.

  8. QSAR analysis and data extrapolation among mammals in a series of aliphatic alcohols

    SciTech Connect

    Tichy, M.; Trcka, V.; Roth, Z.; Krivucova, M.

    1985-09-01

    Concepts of QSAR analysis and biological similarity models are combined for use in extrapolation of LD/sub 50/ values after IP application of a series of aliphatic alcohols (C/sub 1/-C/sub 5/) to mouse, hamster, rat, and guinea pig and rabbit. It has been found that although close correlation exists between LD/sub 50/ values after IP and IV applications for mouse and rat, the QSAR obtained with LD/sub 50/ after IV application are not suitable for a prediction of LD/sub 50/ values after IP application for rabbit. Different transformation or distribution processes in mouse, rat, and rabbit after the two types of applications might be the reason. The LD/sub 50/ values (expressed in mmole/m/sup 2/ of body surface) seem to be independent of mammalian species used (at least within the mouse, rat, hamster, and probably guinea pig series). This fact makes it possible to predict reasonable values of LD/sub 50/ after IP application for rabbit. Expression of toxicity in mmole/m/sup 2/ of body surface may be used in toxicological studies. 24 references, 2 figures, 8 tables.

  9. 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.

  10. 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.

  11. A review of quantitative structure activity relationships (QSARs) for assessing the ecotoxicity of phthalate esters

    SciTech Connect

    Parkerton, T.F.

    1995-12-31

    Dialkyl phthalate esters represent an important class of high production volume, industrial chemicals spanning a wide range of chemical properties. Over the last two decades, numerous studies have been conducted to characterize the ecotoxicity of phthalate esters. The purpose of this presentation is to provide a synthesis of the available ecotoxicity literature using a QSAR paradigm. Results from this analysis provide several important insights. First, a mechanistic explanation is provided to account for the general lack of ecotoxicity observed for higher molecular weight phthalates possessing alkyl chains of six or more carbons. Second, studies that appear as outliers are identified due to either experimental artifacts (e.g., physical effects on daphnids due to testing at concentrations exceeding water solubility) or questionable experimental methods (e.g., toxicity tests based on nominal concentrations). Lastly, differences in ecotoxicity between species appear to be due, in part, to differences in test organisms biotransformation capacities. The utility of adopting a QSAR-based approach for risk assessment will be discussed.

  12. p38 Mitogen-activated protein kinase inhibitors: a review on pharmacophore mapping and QSAR studies.

    PubMed

    Gangwal, Rahul P; Bhadauriya, Anuseema; Damre, Mangesh V; Dhoke, Gaurao V; Sangamwar, Abhay T

    2013-01-01

    p38 mitogen-activated protein (MAP) kinases are the serine/threonine protein kinases, which play a vital role in cellular responses to external stress signals. p38 MAP kinase inhibitors have shown anti-inflammatory effects in the preclinical disease models, primarily through inhibition of the expression of inflammatory mediators. A number of structurally diverse p38 MAP kinase inhibitors have been developed as potential anti-inflammatory agents. Most of the inhibitors have failed in the clinical trials either due to poor pharmacokinetic profile or selectivity issue, which makes p38 MAP kinase a promising target for molecular modelling studies. Several quantitative structure activity relationships (QSAR) and pharmacophore models have been developed to identify the structural requirements essential for p38 MAP kinase inhibitory activity. In this review, we provide an overview of the presently known p38 MAP kinase inhibitors and how QSAR analyses among series of compounds have led to the development of molecular models and pharmacophores, allowing the design of novel inhibitors.

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

    NASA Astrophysics Data System (ADS)

    Cao, Shandong

    2012-08-01

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

  14. 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

  15. Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

    PubMed

    Barber, Chris; Cayley, Alex; Hanser, Thierry; Harding, Alex; Heghes, Crina; Vessey, Jonathan D; Werner, Stephane; Weiner, Sandy K; Wichard, Joerg; Giddings, Amanda; Glowienke, Susanne; Parenty, Alexis; Brigo, Alessandro; Spirkl, Hans-Peter; Amberg, Alexander; Kemper, Ray; Greene, Nigel

    2016-04-01

    The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound. PMID:26708083

  16. Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols.

    PubMed

    Mitra, Indrani; Saha, Achintya; Roy, Kunal

    2011-03-01

    An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico.

  17. Human Mitochondrial Protein Database

    National Institute of Standards and Technology Data Gateway

    SRD 131 Human Mitochondrial Protein Database (Web, free access)   The Human Mitochondrial Protein Database (HMPDb) provides comprehensive data on mitochondrial and human nuclear encoded proteins involved in mitochondrial biogenesis and function. This database consolidates information from SwissProt, LocusLink, Protein Data Bank (PDB), GenBank, Genome Database (GDB), Online Mendelian Inheritance in Man (OMIM), Human Mitochondrial Genome Database (mtDB), MITOMAP, Neuromuscular Disease Center and Human 2-D PAGE Databases. This database is intended as a tool not only to aid in studying the mitochondrion but in studying the associated diseases.

  18. 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.

  19. 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. PMID:27105149

  20. The Norwegian Danish Basin: A key to understanding the Cenozoic in the eastern North Sea

    NASA Astrophysics Data System (ADS)

    Rasmussen, Thomas L.; Clausen, Ole R.; Andresen, Katrine J.; Goledowski, Bartosz

    2015-04-01

    The Danish part of Norwegian-Danish Basin, which constitutes the eastern part of the North Sea Basin, has been the key area for sequence stratigraphic subdivision and analysis of the Cenozoic succession since the mid 1990's. Widespread 3D seismic data, in the central parts of the North Sea Basin, as well as more scattered 3D seismic data in the Danish part of the Norwegian-Danish Basin, have given a more detailed understanding of the sequences and indicate that climate is tenable for the origin of Cenozoic sequence boundaries. The previous sequence stratigraphic interpretations have been an integrated part of an ongoing debate concerning vertical movements of the Fennoscandian shield versus the impact of climate and erosion. A newly accessed coherent regional 2D and reprocessed 3D seismic data set, in the Norwegian part of the Norwegian-Danish Basin, constitute the database for a new sequence stratigraphic analysis of the entire area. The objective of the new study is to test previous subdivisions and introduce a coherent 3D sequence stratigraphic analysis and depositional model for the entire Norwegian-Danish Basin. This analysis is necessary to get out of the stalemate with the uplift discussion. The study shows that the original subdivision by Michelsen et al. (1995, 1998) stands. However, revision of few a sequence boundaries may have to be adjusted due to new biostratigraphic information published. Furthermore, high-angle clinoforms and geomorphological transport complexes observed in the Danish North Sea Basin can be traced into the Norwegian sector. This together with the recognition of several other high-angle clinoform complexes, and their associated seismic facies distribution maps and thickness-maps, enhances the level of detail and constrains the previous published paleogeographic reconstructions of the Cenozoic. The geometry of the Cenozoic infill, in the Norwegian part of the Norwegian-Danish Basin, is here interpreted to be controlled by relative sea

  1. Annoying Danish Relatives: Comprehension and Production of Relative Clauses by Danish Children with and without SLI

    ERIC Educational Resources Information Center

    Jensen De Lopez, Kristine; Olsen, Lone Sundahl; Chondrogianni, Vasiliki

    2014-01-01

    This study examines the comprehension and production of subject and object relative clauses (SRCs, ORCs) by children with Specific Language Impairment (SLI) and their typically developing (TD) peers. The purpose is to investigate whether relative clauses are problematic for Danish children with SLI and to compare errors with those produced by TD…

  2. Educational Ambassadors in the Danish Trade Union Movement

    ERIC Educational Resources Information Center

    Keil, Michael

    2008-01-01

    The concept of Educational Ambassadors is embedded within the so-called "Danish model" of industrial relations. The Danish industrial relations system is characterised by strong collective organisations with national coverage, which conclude the collective agreements for various industries or sectors and which are mostly grouped under central…

  3. 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.

  4. 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. PMID:25271473

  5. Structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) for inhibitor design.

    PubMed

    Du, Qi-Shi; Gao, Jing; Wei, Yu-Tuo; Du, Li-Qin; Wang, Shu-Qing; Huang, Ri-Bo

    2012-04-23

    The inhibitions of enzymes (proteins) are determined by the binding interactions between ligands and targeting proteins. However, traditional QSAR (quantitative structure-activity relationship) is a one-side technique, only considering the structures and physicochemical properties of inhibitors. In this study, the structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) is presented, in which the structural information of host protein is involved in the QSAR calculations. The SB-MP-3D-QSAR actually is a combinational method of docking approach and QSAR technique. Multiple docking calculations are performed first between the host protein and ligand molecules in a training set. In the targeting protein, the functional residues are selected, which make the major contribution to the binding free energy. The binding free energy between ligand and targeting protein is the summation of multiple potential energies, including van der Waals energy, electrostatic energy, hydrophobic energy, and hydrogen-bond energy, and may include nonthermodynamic factors. In the foundational QSAR equation, two sets of weighting coefficients {aj} and {bp} are assigned to the potential energy terms and to the functional residues, respectively. The two coefficient sets are solved by using iterative double least-squares (IDLS) technique in the training set. Then, the two sets of weighting coefficients are used to predict the bioactivities of inquired ligands. In an application example, the new developed method obtained much better results than that of docking calculations.

  6. Developing and Evaluating a Multimodal Course Format: Danish for Knowledge Workers--Labour Market-Related Danish

    ERIC Educational Resources Information Center

    Frederiksen, Karen-Margrete; Laursen, Katja Årosin

    2015-01-01

    This paper presents our reflections on developing the Computer-Assisted Language Learning (CALL) course "Danish for knowledge workers--labour market-related Danish." As defined by Laursen and Frederiksen (2015), knowledge workers are "highly educated people who typically work at universities, at other institutions of higher…

  7. Physiological Information Database (PID)

    EPA Science Inventory

    EPA has developed a physiological information database (created using Microsoft ACCESS) intended to be used in PBPK modeling. The database contains physiological parameter values for humans from early childhood through senescence as well as similar data for laboratory animal spec...

  8. Network II Database

    1994-11-07

    The Oak Ridge National Laboratory (ORNL) Rail and Barge Network II Database is a representation of the rail and barge system of the United States. The network is derived from the Federal Rail Administration (FRA) rail database.

  9. THE ECOTOX DATABASE

    EPA Science Inventory

    The database provides chemical-specific toxicity information for aquatic life, terrestrial plants, and terrestrial wildlife. ECOTOX is a comprehensive ecotoxicology database and is therefore essential for providing and suppoirting high quality models needed to estimate population...

  10. Household Products Database: Pesticides

    MedlinePlus

    ... Names Types of Products Manufacturers Ingredients About the Database FAQ Product Recalls Help Glossary Contact Us More ... holders. Information is extracted from Consumer Product Information Database ©2001-2015 by DeLima Associates. All rights reserved. ...

  11. Risk factors for Campylobacter colonization in Danish broiler flocks, 2010 to 2011.

    PubMed

    Sandberg, M; Sørensen, L L; Steenberg, B; Chowdhury, S; Ersbøll, A K; Alban, L

    2015-03-01

    The objectives of the two studies presented were to estimate the prevalence of Campylobacter-positive farms and flocks and to acquire updated knowledge about risk factors for the introduction of Campylobacter in Danish broiler flocks. In the first study, from September 2010 to September 2011, there were 25 Danish broiler farms visited, and a questionnaire was filled in by a veterinarian/consultant. In the second study, a similar questionnaire was distributed electronically to all Danish broiler farmers (n=164) that were on record with an email address in the Quality Assurance System in the Danish Broiler Production (KIK) database. House- and flock-specific data collected in the surveys were supplemented with information obtained from the KIK database. Data obtained from the two studies were analyzed separately by logistic regression analysis. In both models, the dependent variable was "Campylobacter flock status (positive/negative)," which was based on real-time PCR testing of fecal material from the floor of each broiler house that had been collected preslaughter using a pair of tube gauze "socks." This material was pooled into one sample. Of the 25 farms visited, 17 had delivered Campylobacter-positive flocks during the study period, and eight farms had no Campylobacter-positive flocks. Moreover, the flock prevalence of Campylobacter was 17% (n=418). Data obtained from the electronically distributed survey revealed that 63% (n=71) of the farms were Campylobacter-positive. Further, the flock prevalence of Campylobacter was 14% (n=1,286). The multivariable models from the two sets of data identified the following statistically significant risk factors: summer vs. winter; if the previous flock in the house was positive for Campylobacter vs. if the previous flock in the house was negative; and litter delivered into the house close to the time of arrival of new chickens vs. storing litter on the farm. Furthermore, the data showed that a vertically based ventilation

  12. Aviation Safety Issues Database

    NASA Technical Reports Server (NTRS)

    Morello, Samuel A.; Ricks, Wendell R.

    2009-01-01

    The aviation safety issues database was instrumental in the refinement and substantiation of the National Aviation Safety Strategic Plan (NASSP). The issues database is a comprehensive set of issues from an extremely broad base of aviation functions, personnel, and vehicle categories, both nationally and internationally. Several aviation safety stakeholders such as the Commercial Aviation Safety Team (CAST) have already used the database. This broader interest was the genesis to making the database publically accessible and writing this report.

  13. Scopus database: a review

    PubMed Central

    Burnham, Judy F

    2006-01-01

    The Scopus database provides access to STM journal articles and the references included in those articles, allowing the searcher to search both forward and backward in time. The database can be used for collection development as well as for research. This review provides information on the key points of the database and compares it to Web of Science. Neither database is inclusive, but complements each other. If a library can only afford one, choice must be based in institutional needs. PMID:16522216

  14. Scopus database: a review.

    PubMed

    Burnham, Judy F

    2006-03-08

    The Scopus database provides access to STM journal articles and the references included in those articles, allowing the searcher to search both forward and backward in time. The database can be used for collection development as well as for research. This review provides information on the key points of the database and compares it to Web of Science. Neither database is inclusive, but complements each other. If a library can only afford one, choice must be based in institutional needs.

  15. Mission and Assets Database

    NASA Technical Reports Server (NTRS)

    Baldwin, John; Zendejas, Silvino; Gutheinz, Sandy; Borden, Chester; Wang, Yeou-Fang

    2009-01-01

    Mission and Assets Database (MADB) Version 1.0 is an SQL database system with a Web user interface to centralize information. The database stores flight project support resource requirements, view periods, antenna information, schedule, and forecast results for use in mid-range and long-term planning of Deep Space Network (DSN) assets.

  16. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles.

    PubMed

    Puzyn, Tomasz; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Hu, Xiaoke; Dasari, Thabitha P; Michalkova, Andrea; Hwang, Huey-Min; Toropov, Andrey; Leszczynska, Danuta; Leszczynski, Jerzy

    2011-03-01

    It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

  17. Expert QSAR system for predicting the bioconcentration factor under the REACH regulation.

    PubMed

    Grisoni, Francesca; Consonni, Viviana; Vighi, Marco; Villa, Sara; Todeschini, Roberto

    2016-07-01

    Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework.

  18. QSARs for chemical mutagens from structure: ridge regression fitting and diagnostics.

    PubMed

    Hawkins, Douglas M; Basak, Subhash C; Mills, Denise

    2004-03-01

    QSAR models have been developed for a diverse set of mutagens using computed molecular descriptors. Such models can be used in predicting mutagenicity from structure. All common methods-regression, neural nets, k-nearest neighbors-are 'linear smoothers'-weighted averages of the activities in the calibration data with weights dependent on the descriptors. While they have been studied extensively, a vital but overlooked area is 'case diagnostics', pointers to compounds that are poorly fitted, or are unusually influential in fitting the model. This is particularly true where the measured activity is binary-present or absent. We illustrate the use of numeric and graphic diagnostics, particularly that of the FF plot, with a data set with 508 compounds and 307 structural descriptors used to predict mutagenicity.

  19. QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

    PubMed Central

    Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

    2011-01-01

    Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

  20. Expert QSAR system for predicting the bioconcentration factor under the REACH regulation.

    PubMed

    Grisoni, Francesca; Consonni, Viviana; Vighi, Marco; Villa, Sara; Todeschini, Roberto

    2016-07-01

    Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework. PMID:27152714

  1. QSAR on antiproliferative naphthoquinones based on a conformation-independent approach.

    PubMed

    Duchowicz, Pablo R; Bennardi, Daniel O; Bacelo, Daniel E; Bonifazi, Evelyn L; Rios-Luci, Carla; Padrón, José M; Burton, Gerardo; Misico, Rosana I

    2014-04-22

    The antiproliferative activities of a series of 36 naphthoquinone derivatives were subjected to a Quantitative Structure-Activity Relationships (QSAR) study. For this purpose a panel of four human cancer cell lines was used, namely HBL-100 (breast), HeLa (cervix), SW-1573 (non-small cell lung) and WiDr (colon). A conformation-independent representation of the chemical structure was established in order to avoid leading with the scarce experimental information on X-ray crystal structure of the drug interaction. The 1179 theoretical descriptors derived with E-Dragon and Recon software were simultaneously analyzed through linear regression models based on the Replacement Method variable subset selection technique. The established models were validated and tested through the use of external test sets of compounds, the Leave-One-Out Cross Validation method, Y-Randomization and Applicability Domain analysis.

  2. Synthesis, Evaluation of Anticancer Activity and QSAR Study of Heterocyclic Esters of Caffeic Acid

    PubMed Central

    Hajmohamad Ebrahim Ketabforoosh, Shima; Amini, Mohsen; Vosooghi, Mohsen; Shafiee, Abbas; Azizi, Ebrahim; Kobarfard, Farzad

    2013-01-01

    Caffeic acid phenethyl ester (CAPE) suppresses the growth of transformed cells such as human breast cancer cells, hepatocarcinoma , myeloid leukemia, colorectal cancer cells, fibrosarcoma, glioma and melanoma. A group of heterocyclic esters of caffeic acid was synthesized using Mitsunobu reaction and the esters were subjected to further structural modification by electrooxidation of the catechol ring of caffeic acid esters in the presence of sodium benzenesulfinate and sodium toluensulfinate as nucleophiles. Both heterocyclic esters of caffeic acid and their arylsulfonyl derivatives were evaluated for their cytotoxic activity against HeLa, SK-OV-3, and HT-29 cancer cell lines. HeLa cells showed the highest sensitivity to the compounds and heterocyclic esters with no substituent on catechol ring showed better activity compared to their substituted counterparts. QSAR studies reemphasized the importance of molecular shape of the compounds for their cytotoxic activity. PMID:24523750

  3. Biological Evaluation and 3D-QSAR Studies of Curcumin Analogues as Aldehyde Dehydrogenase 1 Inhibitors

    PubMed Central

    Wang, Hui; Du, Zhiyun; Zhang, Changyuan; Tang, Zhikai; He, Yan; Zhang, Qiuyan; Zhao, Jun; Zheng, Xi

    2014-01-01

    Aldehyde dehydrogenase 1 (ALDH1) is reported as a biomarker for identifying some cancer stem cells, and down-regulation or inhibition of the enzyme can be effective in anti-drug resistance and a potent therapeutic for some tumours. In this paper, the inhibitory activity, mechanism mode, molecular docking and 3D-QSAR (three-dimensional quantitative structure activity relationship) of curcumin analogues (CAs) against ALDH1 were studied. Results demonstrated that curcumin and CAs possessed potent inhibitory activity against ALDH1, and the CAs compound with ortho di-hydroxyl groups showed the most potent inhibitory activity. This study indicates that CAs may represent a new class of ALDH1 inhibitor. PMID:24840575

  4. QSAR OF DISTRIBUTION COEFFICIENTS FOR PU (NO3)062-COMPLEXES USING MOLECULAR MECHANICS

    SciTech Connect

    M. BARR; G. JARVINEN; E. MOODY

    2000-08-01

    Computer-aided modeling has been very successful in the design of chelating ligands for the formation of selective metal complexes. We report herein preliminary efforts to extend the principles developed for ion-specific chelating ligands to the weaker, more diffuse electrostatic interactions between complex anions and dicationic sites of anion-exchange resins. Calculated electrostatic affinity between plutonium (IV) hexanitrato dianions and analogue of dicationic anion-exchange sites correlate well with empirically-determined distribution coefficients. This Quantitative Structure Activity Relationship (QSAR) is useful in the determination of the overall trend within a select series of bifunctional resins and which structural modifications are most likely to be advantageous. Ultimately, we hope to refine this methodology to allow the a priori determination of ion-exchange behavior for abroad class of materials.

  5. Synthesis, antimicrobial, anticancer evaluation and QSAR studies of 3/4-bromo benzohydrazide derivatives.

    PubMed

    Kumar, Pradeep; Narasimhan, Balasubramanian; Ramasamy, Kalavathy; Mani, Vasudevan; Mishra, Rakesh Kumar; Majeed, Abu Bakar Abdul

    2015-01-01

    A series 3/4-bromo-N'-(substituted benzylidene/furan-2-ylmethylene/5-oxopentylidene/3- phenylallylidene)benzohydrazides (1-23) was synthesized and characterized by physicochemical and spectral means. The synthesized compounds were screened for their antimicrobial and anticancer potentials. Antimicrobial activity results indicated that compound 12 (pMICam = 1.67 μM/ml) was the most potent antimicrobial agent. The synthesized benzohydrazides were also having good anticancer potential and compound 22 (IC50 = 1.20 μM μM) was found to be the most potent anticancer agent which was more potent than standard drugs, tetrandrine (IC50 = 1.53) and 5- fluorouracil (IC50 = 4.6 μM). QSAR studies indicated that antimicrobial activity of synthesized compounds was best described by electronic parameter, total energy (Te) and topological parameters, valance zero order molecular connectivity index ((0)χ(v)) and Wiener index (W). PMID:25860177

  6. QMQSAR: utilization of a semiempirical probe potential in a field-based QSAR method.

    PubMed

    Dixon, Steve; Merz, Kenneth M; Lauri, Giorgio; Ianni, James C

    2005-01-15

    A semiempirical quantum mechanical approach is described for the creation of molecular field-based QSAR models from a set of aligned ligand structures. Each ligand is characterized by a set of probe interaction energy (PIE) values computed at various grid points located near the surface of the ligand. Single-point PM3 calculations afford these PIE values, which represents a pool of independent variables from which multilinear regression models of activity are built. The best n-variable fit is determined by constructing an initial regression using standard forward stepwise selection, followed by refinement using a simulated annealing technique. The resulting fit provides an easily interpreted 3D physical model of ligand binding affinity. Validation against three literature datasets demonstrates the ability of the semiempirical potential to model critical binding interactions in diverse systems.

  7. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

    NASA Astrophysics Data System (ADS)

    Puzyn, Tomasz; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Hu, Xiaoke; Dasari, Thabitha P.; Michalkova, Andrea; Hwang, Huey-Min; Toropov, Andrey; Leszczynska, Danuta; Leszczynski, Jerzy

    2011-03-01

    It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

  8. QSAR and SAR studies on the reduction of some aromatic nitro compounds by xanthine oxidase.

    PubMed

    Thakur, Mamta; Thakur, Abhilash; Balasubramanian, Krishnan

    2006-01-01

    This work describes QSAR and SAR studies on the reduction of 27 aromatic nitro compounds by xanthine oxidase using both distance-based topological indices and quantum molecular descriptors along with indicator parameters. The application of a multiple linear regression analysis indicated that a combination of distance-based topological indices with the ad hoc molecular descriptors and the indicator parameters yielded a statistically significant model for the activity, log K (the reduction of aromatic nitro compounds by xanthine oxidase). The final selection of a potential aromatic nitro compound for the reduction by xanthine oxidase is made by quantum molecular modeling. We have found that, among the various parameters, the quantum Mulliken charge parameters on the fourth atom or para position relative to the nitro group correlated best with the activity.

  9. QSAR on antiproliferative naphthoquinones based on a conformation-independent approach.

    PubMed

    Duchowicz, Pablo R; Bennardi, Daniel O; Bacelo, Daniel E; Bonifazi, Evelyn L; Rios-Luci, Carla; Padrón, José M; Burton, Gerardo; Misico, Rosana I

    2014-04-22

    The antiproliferative activities of a series of 36 naphthoquinone derivatives were subjected to a Quantitative Structure-Activity Relationships (QSAR) study. For this purpose a panel of four human cancer cell lines was used, namely HBL-100 (breast), HeLa (cervix), SW-1573 (non-small cell lung) and WiDr (colon). A conformation-independent representation of the chemical structure was established in order to avoid leading with the scarce experimental information on X-ray crystal structure of the drug interaction. The 1179 theoretical descriptors derived with E-Dragon and Recon software were simultaneously analyzed through linear regression models based on the Replacement Method variable subset selection technique. The established models were validated and tested through the use of external test sets of compounds, the Leave-One-Out Cross Validation method, Y-Randomization and Applicability Domain analysis. PMID:24631897

  10. QSAR design of triazolopyridine mGlu2 receptor positive allosteric modulators.

    PubMed

    Tresadern, Gary; Cid, José-Maria; Trabanco, Andrés A

    2014-09-01

    Two QSAR approaches were applied to assist the design and to prioritise the synthesis of new active mGlu2 receptor positive allosteric modulators (PAMs). With the aim to explore a particular point of substitution the models successfully prioritised molecules originating from chemistry ideas and a large virtual library. The two methods, 3D topomer CoMFA and support vector machines with 2D ECFP6 fingerprints, delivered good correlation and success in this prospective application. Fourteen molecules with different substituent decoration were identified by the in silico models and synthesised. They were found to be highly active and their mGlu2 receptor PAM activity (pEC50) was predicted within 0.3 and 0.4log units of error with the two methods. The value of the molecules and the models for the future of the project is discussed. PMID:25086773

  11. Danish experiences on EIA of livestock projects

    SciTech Connect

    Christensen, Per . E-mail: pc@plan.aau.dk

    2006-07-15

    Since its introduction into Danish planning in 1989, Environmental Impact Assessment (EIA) has been widely discussed. At the centre of the debate has been the question of whether EIA actually offered anything new and there has been a great deal of scepticism about the efficacy of the instrument, especially when it comes to livestock projects. In an evaluation of the Danish EIA experience, we have looked more closely at how the EIA instruments function regarding livestock projects. This article addresses both the EIA process as well as the EIA screening. It is demonstrated that the EIA screening in its own right is a kind of regulatory instrument. Examining the assessments made during screening more closely, we conclude that there is still some way to go in order to make the assessment broader and more holistic in accordance with the ambitions set out in the EIA directive to contribute to a more sustainable development. Although the provisions laid down are the same the praxis related to the field has developed at a considerable speed. In order to understand this development we have closely examined how the decisions made by the Nature Protection Board of Appeal (NPBA) have been changed and conclude that these changes definitely address some of the shortcomings found in the evaluation.

  12. 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.

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

    PubMed Central

    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: R2Tr = 0.9576 and RMSETr = 0.0958 for the training set; Q2CV = 0.9239 and RMSECV = 0.1304 for the LOO-CV set as well as Q2Ext = 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. PMID:26417339

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

    PubMed Central

    Beňo, Milan; Farkaš, Robert

    2009-01-01

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

  15. A QSAR approach to model aquatic toxicity of halogenated aromatic hydrocarbons

    SciTech Connect

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

    1994-12-31

    In a previous work, (1) priority classes for risk assessment have been identified among those resulting from grouping the ``High Volume Chemicals (HVC), as listed in Annex 1 of the EEC Council Regulation, according to their structural similarity, suitable for QSAR analysis. Among these priorities, Halogenated Aromatic Hydrocarbons (HAH) were chosen because their high toxicity calls for systematic investigation. In this study, the authors examined all the existing HAH listed in the European Inventory of Existing Chemical Substances and focused on a class of 142 compounds (benzenes and toluenes), including 19 HVC. The aim of this study was: (1) to provide a reliable model for predicting the aquatic toxicity of the considered HAH class; (2) to confirm the adequacy of the use of topological indices as structural descriptors for modeling; and (3) to provide homogeneous, good quality experimental toxicity data, with an eye to HVC. Ninety topological indices (2) have been used for the parameterization of the compounds. A training set was selected, by means of a statistical design, using the first three significant compounds from a PCA. Acute toxicity tests with Daphnia means were carried out to evaluate the aquatic toxicity of HAH. The toxicity for the training set compounds ranged from {minus}0.1 to {minus}1.47 (log I/EC50, C = {mu}M). The QSAR analysis was carried out using the GOLPE (Generating Optimal Linear PLS Estimation) chemometric package (3.4). The final model, constructed with a small set of variables selected form the original ones, showed a high predictive capacity. A number of compounds have been tested to validate the model.

  16. 5D-QSAR for spirocyclic sigma1 receptor ligands by Quasar receptor surface modeling.

    PubMed

    Oberdorf, Christoph; Schmidt, Thomas J; Wünsch, Bernhard

    2010-07-01

    Based on a contiguous and structurally as well as biologically diverse set of 87 sigma(1) ligands, a 5D-QSAR study was conducted in which a quasi-atomistic receptor surface modeling approach (program package Quasar) was applied. The superposition of the ligands was performed with the tool Pharmacophore Elucidation (MOE-package), which takes all conformations of the ligands into account. This procedure led to four pharmacophoric structural elements with aromatic, hydrophobic, cationic and H-bond acceptor properties. Using the aligned structures a 3D-model of the ligand binding site of the sigma(1) receptor was obtained, whose general features are in good agreement with previous assumptions on the receptor structure, but revealed some novel insights since it represents the receptor surface in more detail. Thus, e.g., our model indicates the presence of an H-bond acceptor moiety in the binding site as counterpart to the ligands' cationic ammonium center, rather than a negatively charged carboxylate group. The presented QSAR model is statistically valid and represents the biological data of all tested compounds, including a test set of 21 ligands not used in the modeling process, with very good to excellent accuracy [q(2) (training set, n=66; leave 1/3 out) = 0.84, p(2) (test set, n=21)=0.64]. Moreover, the binding affinities of 13 further spirocyclic sigma(1) ligands were predicted with reasonable accuracy (mean deviation in pK(i) approximately 0.8). Thus, in addition to novel insights into the requirements for binding of spirocyclic piperidines to the sigma(1) receptor, the presented model can be used successfully in the rational design of new sigma(1) ligands.

  17. The NCBI Taxonomy database.

    PubMed

    Federhen, Scott

    2012-01-01

    The NCBI Taxonomy database (http://www.ncbi.nlm.nih.gov/taxonomy) is the standard nomenclature and classification repository for the International Nucleotide Sequence Database Collaboration (INSDC), comprising the GenBank, ENA (EMBL) and DDBJ databases. It includes organism names and taxonomic lineages for each of the sequences represented in the INSDC's nucleotide and protein sequence databases. The taxonomy database is manually curated by a small group of scientists at the NCBI who use the current taxonomic literature to maintain a phylogenetic taxonomy for the source organisms represented in the sequence databases. The taxonomy database is a central organizing hub for many of the resources at the NCBI, and provides a means for clustering elements within other domains of NCBI web site, for internal linking between domains of the Entrez system and for linking out to taxon-specific external resources on the web. Our primary purpose is to index the domain of sequences as conveniently as possible for our user community.

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

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. PMID:25497990

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

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

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

  20. Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening.

    PubMed

    Neves, Bruno J; Dantas, Rafael F; Senger, Mario R; Melo-Filho, Cleber C; Valente, Walter C G; de Almeida, Ana C M; Rezende-Neto, João M; Lima, Elid F C; Paveley, Ross; Furnham, Nicholas; Muratov, Eugene; Kamentsky, Lee; Carpenter, Anne E; Braga, Rodolpho C; Silva-Junior, Floriano P; Andrade, Carolina Horta

    2016-08-11

    Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization. PMID:27396732

  1. 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.

  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. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling.

    PubMed

    Gramatica, Paola; Pilutti, Pamela; Papa, Ester

    2004-01-01

    The rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds is predicted by QSAR modeling. The applied Multiple Linear Regression is based on a variety of theoretical molecular descriptors, selected by the Genetic Algorithms-Variable Subset Selection (GA-VSS) procedure. The models were validated for predictivity by both internal and external validation. For the external validation two splitting approaches, D-optimal Experimental Design and Kohonen Artificial Neural Networks (K-ANN), were applied to the original data set to compare the two methodologies. We emphasize that external validation is the only way to establish a reliable QSAR model for predictive purposes. Predicted data by consensus modeling from different models are also proposed.

  4. 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. PMID:23756470

  5. QSAR study of some pyrazolo[3,4-d]pyrimidine derivatives as the c-Src inhibitors

    NASA Astrophysics Data System (ADS)

    Shukla, Bindesh Kumar; Yadava, Umesh

    2016-05-01

    Two dimensional quantitative structure activity relationship (QSAR) studies have been carried out on a series of 42 pyrazolo[3,4-d]pyrimidine derivatives to find out the structural requirements for the inhibition of c-SRC phosphorilation. The best predictions were obtained using Heuristic and Best MLR methods from the model where 33 compounds were considered in the training set and the remaining 9 in the test set. Both Best MLR and Heuristic methods indicate that squared correlation coefficient for training and test sets are very close to observed biological activities which designate the good correlation between the experimental and predicted activity. The results that are obtained from 2D-QSAR studies may provide useful insights into the roles of various substitution patterns on the pyrazolo[3,4-d]pyrimidine core and may also help to design more potent compounds.

  6. Residential Radon and Brain Tumour Incidence in a Danish Cohort

    PubMed Central

    Bräuner, Elvira V.; Andersen, Zorana J.; Andersen, Claus E.; Pedersen, Camilla; Gravesen, Peter; Ulbak, Kaare; Hertel, Ole; Loft, Steffen; Raaschou-Nielsen, Ole

    2013-01-01

    Background Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. Objective To investigate the long-term effect of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. Methods During 1993–1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced residential addresses from 1 January 1971 until 31 December 2009 and calculated radon concentrations at each address using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate-ratios (IRR) and 95% confidence intervals (CI) for the risk of primary brain tumours associated with residential radon exposure with adjustment for age, sex, occupation, fruit and vegetable consumption and traffic-related air pollution. Effect modification by air pollution was assessed. Results Median estimated radon was 40.5 Bq/m3. The adjusted IRR for primary brain tumour associated with each 100 Bq/m3 increment in average residential radon levels was 1.96 (95% CI: 1.07; 3.58) and this was exposure-dependently higher over the four radon exposure quartiles. This association was not modified by air pollution. Conclusions We found significant associations and exposure-response patterns between long-term residential radon exposure radon in a general population and risk of primary brain tumours, adding new knowledge to this field. This finding could be chance and needs to be challenged in future studies. PMID:24066143

  7. Residential radon and lung cancer incidence in a Danish cohort

    SciTech Connect

    Braeuner, Elvira V.; Andersen, Claus E.; Sorensen, Mette; Jovanovic Andersen, Zorana; Gravesen, Peter; Ulbak, Kaare; Hertel, Ole; Pedersen, Camilla; Overvad, Kim; Tjonneland, Anne; Raaschou-Nielsen, Ole

    2012-10-15

    High-level occupational radon exposure is an established risk factor for lung cancer. We assessed the long-term association between residential radon and lung cancer risk using a prospective Danish cohort using 57,053 persons recruited during 1993-1997. We followed each cohort member for cancer occurrence until 27 June 2006, identifying 589 lung cancer cases. We traced residential addresses from 1 January 1971 until 27 June 2006 and calculated radon at each of these addresses using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) for lung cancer risk associated with residential radon exposure with and without adjustment for sex, smoking variables, education, socio-economic status, occupation, body mass index, air pollution and consumption of fruit and alcohol. Potential effect modification by sex, traffic-related air pollution and environmental tobacco smoke was assessed. Median estimated radon was 35.8 Bq/m{sup 3}. The adjusted IRR for lung cancer was 1.04 (95% CI: 0.69-1.56) in association with a 100 Bq/m{sup 3} higher radon concentration and 1.67 (95% CI: 0.69-4.04) among non-smokers. We found no evidence of effect modification. We find a positive association between radon and lung cancer risk consistent with previous studies but the role of chance cannot be excluded as these associations were not statistically significant. Our results provide valuable information at the low-level radon dose range.

  8. Residential radon and lung cancer incidence in a Danish cohort.

    PubMed

    Bräuner, Elvira V; Andersen, Claus E; Sørensen, Mette; Andersen, Zorana Jovanovic; Gravesen, Peter; Ulbak, Kaare; Hertel, Ole; Pedersen, Camilla; Overvad, Kim; Tjønneland, Anne; Raaschou-Nielsen, Ole

    2012-10-01

    High-level occupational radon exposure is an established risk factor for lung cancer. We assessed the long-term association between residential radon and lung cancer risk using a prospective Danish cohort using 57,053 persons recruited during 1993-1997. We followed each cohort member for cancer occurrence until 27 June 2006, identifying 589 lung cancer cases. We traced residential addresses from 1 January 1971 until 27 June 2006 and calculated radon at each of these addresses using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) for lung cancer risk associated with residential radon exposure with and without adjustment for sex, smoking variables, education, socio-economic status, occupation, body mass index, air pollution and consumption of fruit and alcohol. Potential effect modification by sex, traffic-related air pollution and environmental tobacco smoke was assessed. Median estimated radon was 35.8 Bq/m(3). The adjusted IRR for lung cancer was 1.04 (95% CI: 0.69-1.56) in association with a 100 Bq/m(3) higher radon concentration and 1.67 (95% CI: 0.69-4.04) among non-smokers. We found no evidence of effect modification. We find a positive association between radon and lung cancer risk consistent with previous studies but the role of chance cannot be excluded as these associations were not statistically significant. Our results provide valuable information at the low-level radon dose range.

  9. An Introduction to Database Structure and Database Machines.

    ERIC Educational Resources Information Center

    Detweiler, Karen

    1984-01-01

    Enumerates principal management objectives of database management systems (data independence, quality, security, multiuser access, central control) and criteria for comparison (response time, size, flexibility, other features). Conventional database management systems, relational databases, and database machines used for backend processing are…

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

    PubMed

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

    2005-05-01

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

  11. A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors

    PubMed Central

    2013-01-01

    Background Tremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients. Results In the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46. Conclusion The combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses. PMID:23683267

  12. 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

  13. 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.

  14. Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2015-01-01

    Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency. PMID:25961517

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

    PubMed

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

    2010-05-01

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

  16. QSARS for predicting biotic and abiotic reductive transformation rate constants of halogenated hydrocarbons in anoxic sediment systems

    SciTech Connect

    Peijnenburg, W.J.G.M.; 't Hart, M.J.; den Hollander, H.A.; van de Meent, D.; Verboom, H.H.

    1991-01-01

    Quantitative structure-activity relationships (QSARs) are developed relating biotic and abiotic pseudo-first-order disappearance rate constants of halogenated hydrocarbons in anoxic sediments to a number of readily available molecular descriptors. Based upon knowledge of the underlying reaction mechanisms, four descriptors were selected: carbon halogen bond strength, the summation of the Hammett (aromatics) and Taft (aliphatics) sigma constants and the inductive constants (aromatics) of the additional substituents, carbon-carbon bond dissociation energy (aliphatics), and steric factors of the additional substituents. Comparison of the abiotic and biotic QSARs clearly showed the close similarities between both processes. By correlating the rate constants for reduction of a number of halocarbons obtained in a number of distinct sediment samples to the organic carbon content of the samples, the QSARs were made operative for predicting rates of reduction of given halocarbons in given sediment-water systems. The correlations were enhanced by taking into account the fraction of the compounds sorbed to the solid phase. (Copyright (c) 1991 Elsevier Science Publishers B.V.)

  17. Docking and 3-D QSAR studies on the binding of tetrahydropyrimid-2-one HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Rao, Shashidhar N.; Balaji, Govardhan A.; Balaji, Vitukudi N.

    2013-06-01

    We present molecular docking and 3-D QSAR studies on a series of tetrahydropyrimid-2-one HIV-1 protease inhibitors whose binding affinities to the enzyme span nearly 6 orders of magnitude. The docking investigations have been carried out with Surflex (GEOM, GEOMX) and Glide (SP and XP) methodologies available through Tripos and Schrodinger suite of tools in the context of Sybyl-X and Maestro interfaces, respectively. The alignments for 3-D QSAR studies were obtained by using the automated Surflex-SIM methodology in Sybyl-X and the analyses were performed using the CoMFA and CoMSIA methods. Additionally, the top-ranked poses obtained from various docking protocols were also employed to generate CoMFA and CoMSIA models to evaluate the qualitative consistency of the docked models with experimental data. Our studies demonstrate that while there are a number of common features in the docked models obtained from Surflex-dock and Glide methodologies, the former sets of models are generally better correlated with deduced experimental binding modes based on the X-ray structures of known HIV-1 protease complexes with cyclic ureas. The urea moiety common to all the ligands are much more tightly aligned in Surflex docked structures than in the models obtained from Glide SP and XP dockings. The 3-D QSAR models are qualitatively and quantitatively similar to those previously reported, suggesting the utility of automatically generated alignments from Surflex-SIM methodology.

  18. Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2013-01-01

    Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

  19. Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses.

    PubMed

    Masand, Vijay H; Mahajan, Devidas T; Alafeefy, Ahmed M; Bukhari, Syed Nasir Abbas; Elsayed, Nahed N

    2015-09-18

    Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs. PMID:26066412

  20. Identification of 3-Nitro-2,4,6-trihydroxybenzamide Derivatives as Photosynthetic Electron Transport Inhibitors by QSAR and Pharmacophore Studies.

    PubMed

    Sharma, Mukesh C

    2016-06-01

    In the present investigation, quantitative structure-activity relationship (QSAR) analysis was performed on a data set consisting of structurally diverse compounds in order to investigate the role of their structural features on their photosynthetic electron transport Inhibitors. The best 2D-QSAR model was selected, having correlation coefficient r (2) = 0.8544 and cross-validated squared correlation coefficient q (2) = 0.7139 with external predictive ability of pred_r (2) = 0.7753. The results obtained in this study indicate that the presence of hydroxy and nitro groups, expressed by the SsOHcount and SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of [Formula: see text] and [Formula: see text]. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature. The developed model was found to be predictive and can be used to design potent photosynthetic electron transport activities prior to their synthesis for further lead modification.

  1. Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction.

    PubMed

    Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; Rahmberg, Magnus; Nilsson, Sara; Sahlin, Ullrika; Jeliazkova, Nina; Kochev, Nikolay; Pukalov, Ognyan; Tetko, Igor; Brandmaier, Stefan; Durjava, Mojca Kos; Kolar, Boris; Peijnenburg, Willie; Gramatica, Paola

    2013-03-01

    QSAR regression models of the toxicity of triazoles and benzotriazoles ([B]TAZs) to an alga (Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by five partners in the FP7-EU Project, CADASTER. The models were developed by different methods - Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative Neural Network (ASNN) - by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPR-THESAURUS web). In addition, different procedures were used for variable selection, validation and applicability domain inspection. The predictions of the models developed, as well as those obtained in a consensus approach by averaging the data predicted from each model, were compared with the results of experimental tests that were performed by two CADASTER partners. The individual and consensus models were able to correctly predict the toxicity classes of the chemicals tested in the CADASTER project, confirming the utility of the QSAR approach. The models were also used for the prediction of aquatic toxicity of over 300 (B)TAZs, many of which are included in the REACH pre-registration list, and were without experimental data. This highlights the importance of QSAR models for the screening and prioritisation of untested chemicals, in order to reduce and focus experimental testing.

  2. Target Based Designing of Anthracenone Derivatives as Tubulin Polymerization Inhibiting Agents: 3D QSAR and Docking Approach

    PubMed Central

    Naffaa, Moawiah M.; Bakht, Mohammed Afroz; Malhotra, Manav; Ganaie, Majid A.

    2014-01-01

    Novel anthracenone derivatives were designed through in silico studies including 3D QSAR, pharmacophore mapping, and molecular docking approaches. Tubulin protein was explored for the residues imperative for activity by analyzing the binding pattern of colchicine and selected compounds of anthracenone derivatives in the active domain. The docking methodology applied in the study was first validated by comparative evaluation of the predicted and experimental inhibitory activity. Furthermore, the essential features responsible for the activity were established by carrying out pharmacophore mapping studies. 3D QSAR studies were carried out for a series of 1,5- and 1,8-disubstituted10-benzylidene-10H-anthracen-9-ones and 10-(2-oxo-2-phenylethylidene)-10H-anthracen-9-one derivatives for their antiproliferation activity. Based on the pattern recognition studies obtained from QSAR results, ten novel compounds were designed and docked in the active domain of tubulin protein. One of the novel designed compounds “N1” exhibited binding energy −9.69 kcal/mol and predicted Ki 78.32 nM which was found to be better than colchicine. PMID:25383219

  3. A QSAR study on the inhibition mechanism of matrix metalloproteinase-12 by arylsulfone analogs based on molecular orbital calculations.

    PubMed

    Hitaoka, Seiji; Chuman, Hiroshi; Yoshizawa, Kazunari

    2015-01-21

    A binding mechanism between human matrix metalloproteinase-12 (MMP-12) and eight arylsulfone analogs having two types of carboxylic and hydroxamic acids as the most representative zinc binding group is investigated using a quantitative structure-activity relationship (QSAR) analysis based on a linear expression by representative energy terms (LERE). The LERE-QSAR analysis quantitatively reveals that the variation in the observed (experimental) inhibitory potency among the arylsulfone analogs is decisively governed by those in the intrinsic binding and dispersion interaction energies. The results show that the LERE-QSAR analysis not only can excellently reproduce the observed overall free-energy change but also can determine the contributions of representative free-energy changes. An inter-fragment interaction energy difference (IFIED) analysis based on the fragment molecular orbital (FMO) method (FMO-IFIED) leads to the identification of key residues governing the variation in the inhibitory potency as well as to the understanding of the difference between the interactions of the carboxylic and hydroxamic acid zinc binding groups. The current results that have led to the optimization of the inhibitory potency of arylsulfone analogs toward MMP-12 to be used in the treatment of chronic obstructive pulmonary disease may be useful for the development of a new potent MMP-12 inhibitor.

  4. 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

  5. U.S. EPA`s use of QSAR for new and existing chemical evaluations: Regulatory aspects

    SciTech Connect

    Zeeman, M.; Clements, R.G.; Nabholz, J.V.; Auer, C.M.

    1994-12-31

    As up front testing is not required, ecotoxicity or fate data are available for only about 5% of the 2,200 new chemicals/year submitted to EPA. The EPA`s Toxic Substances Control Act (TSCA) Inventory of existing chemicals currently lists about 72,000 chemicals. Most of existing chemicals also appear to have little or no ecotoxicity or fate data available. The EPA`s Office of Pollution Prevention and Toxics (OPPT) has developed and relies upon QSARs to estimate the ecotoxicity and fate of most of the new chemicals evaluated for ecological hazard and risk assessment. QSAR methods routinely result in ecotoxicity estimations of acute and sub/chronic toxicity to fish, aquatic invertebrates, and algae, and fate estimations of physical/chemical properties, degradation, and bioconcentration. GSAR methods were also used to screen the TSCA Inventory for the ecotoxicity of specific chemical classes of over 8,000 discrete chemicals. In the latter effort, seven major chemical classes made up almost 95% of these chemicals. From this QSAR analysis, about 10--20% or 15--18% of these chemicals, respectively, are expected to have high acute or high chronic toxicity to aquatic species. Estimates of ecotoxicity for > 8,000 discrete organics enhances prioritization of chemicals on the TSCA Inventory and helps focus EPA/OPPT regulatory efforts upon the potentially most toxic substances.

  6. 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.

  7. 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.

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

    PubMed

    Papamokos, George; Silins, Ilona

    2016-01-01

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

  9. Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents.

    PubMed

    Speck-Planche, Alejandro; Cordeiro, M N D S

    2013-01-01

    Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections. PMID:24200354

  10. 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.

  11. Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands.

    PubMed

    Castillo-González, Daimel; Mergny, Jean-Louis; De Rache, Aurore; Pérez-Machado, Gisselle; Cabrera-Pérez, Miguel Angel; Nicolotti, Orazio; Introcaso, Antonellina; Mangiatordi, Giuseppe Felice; Guédin, Aurore; Bourdoncle, Anne; Garrigues, Teresa; Pallardó, Federico; Cordeiro, M Natália D S; Paz-Y-Miño, Cesar; Tejera, Eduardo; Borges, Fernanda; Cruz-Monteagudo, Maykel

    2015-10-26

    Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 μM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.

  12. ITS-90 Thermocouple Database

    National Institute of Standards and Technology Data Gateway

    SRD 60 NIST ITS-90 Thermocouple Database (Web, free access)   Web version of Standard Reference Database 60 and NIST Monograph 175. The database gives temperature -- electromotive force (emf) reference functions and tables for the letter-designated thermocouple types B, E, J, K, N, R, S and T. These reference functions have been adopted as standards by the American Society for Testing and Materials (ASTM) and the International Electrotechnical Commission (IEC).

  13. 2010 Worldwide Gasification Database

    DOE Data Explorer

    The 2010 Worldwide Gasification Database describes the current world gasification industry and identifies near-term planned capacity additions. The database lists gasification projects and includes information (e.g., plant location, number and type of gasifiers, syngas capacity, feedstock, and products). The database reveals that the worldwide gasification capacity has continued to grow for the past several decades and is now at 70,817 megawatts thermal (MWth) of syngas output at 144 operating plants with a total of 412 gasifiers.

  14. Can Entrepreneurship Be Taught? A Danish Case Study.

    ERIC Educational Resources Information Center

    Heeboll, John

    1997-01-01

    Reviews a Japanese study linking practical experience for entrepreneurship students to business start-up. Describes a Danish endeavor to revitalize entrepreneurial culture through educational and industrial development programs. (SK)

  15. Explicit Sex--Liberation or Exploitation: Danish "Permissiveness" Revisited

    ERIC Educational Resources Information Center

    Bachy, Victor

    1976-01-01

    Reviews various Danish legislative actions leading up to the lifting of the ban on pornography, and discusses possible consequences of such liberalization by analyzing police statistics from a five year period. (MH)

  16. Hereditary angioneurotic edema and HLA types in two Danish families.

    PubMed

    Eggert, J; Zachariae, H; Svejgaard, E; Svejgaard, A; Kissmeyer-Nielsen, F

    1982-01-01

    HLA types were determined in 19 patients and 9 healthy members of 2 Danish families with hereditary angioneurotic edema. The study revealed no connections between hereditary angioneurotic edema and the HLA system. PMID:7165360

  17. Is the Danish wind energy model replicable for other countries?

    SciTech Connect

    Sovacool, Benjamin K.; Lindboe, Hans H.; Odgaard, Ole

    2008-03-15

    Though aspects of the Danish wind energy model are unique, policymakers might do well to imitate such aspects as a strong political commitment, consistent policy mechanisms, and an incremental, ''hands-on'' approach to R and D. (author)

  18. Databases for Microbiologists

    DOE PAGES

    Zhulin, Igor B.

    2015-05-26

    Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. Finally, the purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists.

  19. Veterans Administration Databases

    Cancer.gov

    The Veterans Administration Information Resource Center provides database and informatics experts, customer service, expert advice, information products, and web technology to VA researchers and others.

  20. Databases for Microbiologists

    PubMed Central

    2015-01-01

    Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. The purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists. PMID:26013493

  1. Databases for LDEF results

    NASA Technical Reports Server (NTRS)

    Bohnhoff-Hlavacek, Gail

    1992-01-01

    One of the objectives of the team supporting the LDEF Systems and Materials Special Investigative Groups is to develop databases of experimental findings. These databases identify the hardware flown, summarize results and conclusions, and provide a system for acknowledging investigators, tracing sources of data, and future design suggestions. To date, databases covering the optical experiments, and thermal control materials (chromic acid anodized aluminum, silverized Teflon blankets, and paints) have been developed at Boeing. We used the Filemaker Pro software, the database manager for the Macintosh computer produced by the Claris Corporation. It is a flat, text-retrievable database that provides access to the data via an intuitive user interface, without tedious programming. Though this software is available only for the Macintosh computer at this time, copies of the databases can be saved to a format that is readable on a personal computer as well. Further, the data can be exported to more powerful relational databases, capabilities, and use of the LDEF databases and describe how to get copies of the database for your own research.

  2. [Method of traditional Chinese medicine formula design based on 3D-database pharmacophore search and patent retrieval].

    PubMed

    He, Yu-su; Sun, Zhi-yi; Zhang, Yan-ling

    2014-11-01

    By using the pharmacophore model of mineralocorticoid receptor antagonists as a starting point, the experiment stud- ies the method of traditional Chinese medicine formula design for anti-hypertensive. Pharmacophore models were generated by 3D-QSAR pharmacophore (Hypogen) program of the DS3.5, based on the training set composed of 33 mineralocorticoid receptor antagonists. The best pharmacophore model consisted of two Hydrogen-bond acceptors, three Hydrophobic and four excluded volumes. Its correlation coefficient of training set and test set, N, and CAI value were 0.9534, 0.6748, 2.878, and 1.119. According to the database screening, 1700 active compounds from 86 source plant were obtained. Because of lacking of available anti-hypertensive medi cation strategy in traditional theory, this article takes advantage of patent retrieval in world traditional medicine patent database, in order to design drug formula. Finally, two formulae was obtained for antihypertensive. PMID:25850277

  3. Forwards or backwards? New directions in Danish patients' rights legislation.

    PubMed

    Hartlev, Mette

    2011-09-01

    The Danish Patients' Rights Act from 1998 was the first comprehensive piece of legislation addressing the basic legal values and principles governing the relation between patient and the health care services. Since the adoption of the Act there has been continuous legislative activity in the field, and the objective of the article is to discuss how recent developments in Danish patients' rights legislation shall be interpreted in terms of balancing interests of patients towards interests of society and the health care professions.

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

    PubMed

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

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors. PMID:25046176

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

    PubMed

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

    2014-10-01

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

  6. 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.

  7. New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors.

    PubMed

    Marchand-Geneste, Nathalie; Watson, Kimberly A; Alsberg, Bjørn K; King, Ross D

    2002-01-17

    A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.

  8. 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

  9. Database in Artificial Intelligence.

    ERIC Educational Resources Information Center

    Wilkinson, Julia

    1986-01-01

    Describes a specialist bibliographic database of literature in the field of artificial intelligence created by the Turing Institute (Glasgow, Scotland) using the BRS/Search information retrieval software. The subscription method for end-users--i.e., annual fee entitles user to unlimited access to database, document provision, and printed awareness…

  10. BioImaging Database

    SciTech Connect

    David Nix, Lisa Simirenko

    2006-10-25

    The Biolmaging Database (BID) is a relational database developed to store the data and meta-data for the 3D gene expression in early Drosophila embryo development on a cellular level. The schema was written to be used with the MySQL DBMS but with minor modifications can be used on any SQL compliant relational DBMS.

  11. Biological Macromolecule Crystallization Database

    National Institute of Standards and Technology Data Gateway

    SRD 21 Biological Macromolecule Crystallization Database (Web, free access)   The Biological Macromolecule Crystallization Database and NASA Archive for Protein Crystal Growth Data (BMCD) contains the conditions reported for the crystallization of proteins and nucleic acids used in X-ray structure determinations and archives the results of microgravity macromolecule crystallization studies.

  12. Online Database Searching Workbook.

    ERIC Educational Resources Information Center

    Littlejohn, Alice C.; Parker, Joan M.

    Designed primarily for use by first-time searchers, this workbook provides an overview of online searching. Following a brief introduction which defines online searching, databases, and database producers, five steps in carrying out a successful search are described: (1) identifying the main concepts of the search statement; (2) selecting a…

  13. Ionic Liquids Database- (ILThermo)

    National Institute of Standards and Technology Data Gateway

    SRD 147 Ionic Liquids Database- (ILThermo) (Web, free access)   IUPAC Ionic Liquids Database, ILThermo, is a free web research tool that allows users worldwide to access an up-to-date data collection from the publications on experimental investigations of thermodynamic, and transport properties of ionic liquids as well as binary and ternary mixtures containing ionic liquids.

  14. HIV Structural Database

    National Institute of Standards and Technology Data Gateway

    SRD 102 HIV Structural Database (Web, free access)   The HIV Protease Structural Database is an archive of experimentally determined 3-D structures of Human Immunodeficiency Virus 1 (HIV-1), Human Immunodeficiency Virus 2 (HIV-2) and Simian Immunodeficiency Virus (SIV) Proteases and their complexes with inhibitors or products of substrate cleavage.

  15. Morchella MLST database

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Welcome to the Morchella MLST database. This dedicated database was set up at the CBS-KNAW Biodiversity Center by Vincent Robert in February 2012, using BioloMICS software (Robert et al., 2011), to facilitate DNA sequence-based identifications of Morchella species via the Internet. The current datab...

  16. Atomic Spectra Database (ASD)

    National Institute of Standards and Technology Data Gateway

    SRD 78 NIST Atomic Spectra Database (ASD) (Web, free access)   This database provides access and search capability for NIST critically evaluated data on atomic energy levels, wavelengths, and transition probabilities that are reasonably up-to-date. The NIST Atomic Spectroscopy Data Center has carried out these critical compilations.

  17. First Look: TRADEMARKSCAN Database.

    ERIC Educational Resources Information Center

    Fernald, Anne Conway; Davidson, Alan B.

    1984-01-01

    Describes database produced by Thomson and Thomson and available on Dialog which contains over 700,000 records representing all active federal trademark registrations and applications for registrations filed in United States Patent and Trademark Office. A typical record, special features, database applications, learning to use TRADEMARKSCAN, and…

  18. Dictionary as Database.

    ERIC Educational Resources Information Center

    Painter, Derrick

    1996-01-01

    Discussion of dictionaries as databases focuses on the digitizing of The Oxford English dictionary (OED) and the use of Standard Generalized Mark-Up Language (SGML). Topics include the creation of a consortium to digitize the OED, document structure, relational databases, text forms, sequence, and discourse. (LRW)

  19. Structural Ceramics Database

    National Institute of Standards and Technology Data Gateway

    SRD 30 NIST Structural Ceramics Database (Web, free access)   The NIST Structural Ceramics Database (WebSCD) provides evaluated materials property data for a wide range of advanced ceramics known variously as structural ceramics, engineering ceramics, and fine ceramics.

  20. Build Your Own Database.

    ERIC Educational Resources Information Center

    Jacso, Peter; Lancaster, F. W.

    This book is intended to help librarians and others to produce databases of better value and quality, especially if they have had little previous experience in database construction. Drawing upon almost 40 years of experience in the field of information retrieval, this book emphasizes basic principles and approaches rather than in-depth and…

  1. Knowledge Discovery in Databases.

    ERIC Educational Resources Information Center

    Norton, M. Jay

    1999-01-01

    Knowledge discovery in databases (KDD) revolves around the investigation and creation of knowledge, processes, algorithms, and mechanisms for retrieving knowledge from data collections. The article is an introductory overview of KDD. The rationale and environment of its development and applications are discussed. Issues related to database design…

  2. Database Searching by Managers.

    ERIC Educational Resources Information Center

    Arnold, Stephen E.

    Managers and executives need the easy and quick access to business and management information that online databases can provide, but many have difficulty articulating their search needs to an intermediary. One possible solution would be to encourage managers and their immediate support staff members to search textual databases directly as they now…

  3. A Quality System Database

    NASA Technical Reports Server (NTRS)

    Snell, William H.; Turner, Anne M.; Gifford, Luther; Stites, William

    2010-01-01

    A quality system database (QSD), and software to administer the database, were developed to support recording of administrative nonconformance activities that involve requirements for documentation of corrective and/or preventive actions, which can include ISO 9000 internal quality audits and customer complaints.

  4. Assignment to database industy

    NASA Astrophysics Data System (ADS)

    Abe, Kohichiroh

    Various kinds of databases are considered to be essential part in future large sized systems. Information provision only by databases is also considered to be growing as the market becomes mature. This paper discusses how such circumstances have been built and will be developed from now on.

  5. Cascadia Tsunami Deposit Database

    USGS Publications Warehouse

    Peters, Robert; Jaffe, Bruce; Gelfenbaum, Guy; Peterson, Curt

    2003-01-01

    The Cascadia Tsunami Deposit Database contains data on the location and sedimentological properties of tsunami deposits found along the Cascadia margin. Data have been compiled from 52 studies, documenting 59 sites from northern California to Vancouver Island, British Columbia that contain known or potential tsunami deposits. Bibliographical references are provided for all sites included in the database. Cascadia tsunami deposits are usually seen as anomalous sand layers in coastal marsh or lake sediments. The studies cited in the database use numerous criteria based on sedimentary characteristics to distinguish tsunami deposits from sand layers deposited by other processes, such as river flooding and storm surges. Several studies cited in the database contain evidence for more than one tsunami at a site. Data categories include age, thickness, layering, grainsize, and other sedimentological characteristics of Cascadia tsunami deposits. The database documents the variability observed in tsunami deposits found along the Cascadia margin.

  6. Analysis of matches and partial-matches in a Danish STR data set.

    PubMed

    Tvedebrink, Torben; Eriksen, Poul Svante; Curran, James Michael; Mogensen, Helle Smidt; Morling, Niels

    2012-05-01

    Over the recent years, the national databases of STR profiles have grown in size due to the success of forensic DNA analysis in solving crimes. The accumulation of DNA profiles implies that the probability of a random match or near match of two randomly selected DNA profiles in the database increases. We analysed 53,295 STR profiles from individuals investigated in relation to crime case investigations at the Department of Forensic Medicine, Faculty of Health Sciences, University of Copenhagen, Denmark. Incomplete STR profiles (437 circa 0.8% of the total), 48 redundant STR profiles from monozygotic twins (0.09%), 6 redundant STR profiles of unknown cause and 1283 STR profiles from repeated testing of individuals were removed leaving 51,517 complete 10 locus STR profiles for analysis. The number corresponds to approximately 1% of the Danish population. We compared all STR profiles to each other, i.e. 1.3×10(9) comparisons. With these large number of comparisons, it is likely to observe DNA profiles that coincide on many loci, which has concerned some commentators and raised questions about "overstating" the power of DNA evidence. We used the method of Weir [11,12] and Curran et al. [3] to compare the observed and expected number of matches and near matches in the data set. We extended the methods by computing the covariance matrix of the summary statistic and used it for the estimation of the identical-by-descent parameter, θ. The analysis demonstrated a number of close relatives in the Danish data set and substructure. The main contribution to the substructure comes from close relatives. An overall θ-value of 1% compensated for the observed substructure, when close familial relationships were accounted for. PMID:21900065

  7. Analysis of matches and partial-matches in a Danish STR data set.

    PubMed

    Tvedebrink, Torben; Eriksen, Poul Svante; Curran, James Michael; Mogensen, Helle Smidt; Morling, Niels

    2012-05-01

    Over the recent years, the national databases of STR profiles have grown in size due to the success of forensic DNA analysis in solving crimes. The accumulation of DNA profiles implies that the probability of a random match or near match of two randomly selected DNA profiles in the database increases. We analysed 53,295 STR profiles from individuals investigated in relation to crime case investigations at the Department of Forensic Medicine, Faculty of Health Sciences, University of Copenhagen, Denmark. Incomplete STR profiles (437 circa 0.8% of the total), 48 redundant STR profiles from monozygotic twins (0.09%), 6 redundant STR profiles of unknown cause and 1283 STR profiles from repeated testing of individuals were removed leaving 51,517 complete 10 locus STR profiles for analysis. The number corresponds to approximately 1% of the Danish population. We compared all STR profiles to each other, i.e. 1.3×10(9) comparisons. With these large number of comparisons, it is likely to observe DNA profiles that coincide on many loci, which has concerned some commentators and raised questions about "overstating" the power of DNA evidence. We used the method of Weir [11,12] and Curran et al. [3] to compare the observed and expected number of matches and near matches in the data set. We extended the methods by computing the covariance matrix of the summary statistic and used it for the estimation of the identical-by-descent parameter, θ. The analysis demonstrated a number of close relatives in the Danish data set and substructure. The main contribution to the substructure comes from close relatives. An overall θ-value of 1% compensated for the observed substructure, when close familial relationships were accounted for.

  8. House dust in seven Danish offices

    NASA Astrophysics Data System (ADS)

    Mølhave, L.; Schneider, T.; Kjærgaard, S. K.; Larsen, L.; Norn, S.; Jørgensen, O.

    Floor dust from Danish offices was collected and analyzed. The dust was to be used in an exposure experiment. The dust was analyzed to show the composition of the dust which can be a source of airborne dust indoors. About 11 kg of dust from vacuum cleaner bags from seven Danish office buildings with about 1047 occupants (12 751 m 2) was processed according to a standardized procedure yielding 5.5 kg of processed bulk dust. The bulk dust contained 130.000-160.000 CFU g -1 microorganisms and 71.000-90.000 CFU g -1 microfungi. The content of culturable microfungi was 65-123 CFU 30 g -1 dust. The content of endotoxins ranged from 5.06-7.24 EU g -1 (1.45 ng g -1 to 1.01 ng g -1). Allergens (ng g -1) were from 147-159 (Mite), 395-746 (dog) and 103-330 (cat). The macro molecular organic compounds (the MOD-content) varied from 7.8-9.8 mg g -1. The threshold of release of histamine from basophil leukocytes provoked by the bulk dust was between 0.3 and 1.0 mg ml -1. The water content was 2% (WGT) and the organic fraction 33%. 6.5-5.9% (dry) was water soluble. The fiber content was less than 0.2-1.5% (WGT) and the desorbable VOCs was 176-319 μg g -1. Most of the VOC were aldehydes. However, softeners for plastic (DBP and DEHP) were present. The chemical composition includes human and animal skin fragments, paper fibers, glass wool, wood and textilefibers and inorganic and metal particles. The sizes ranged from 0.001-1 mm and the average specific density was 1.0 g m -3. The bulk dust was resuspended and injected into an exposure chamber. The airborne dust was sampled and analyzed to illustrate the exposures that can result from sedimented dirt and dust. The airborne dust resulting from the bulk dust reached concentrations ranging from 0.26-0.75 mg m -3 in average contained 300-170 CFU m -3. The organic fraction was from 55-70% and the water content about 2.5% (WGT). The content of the dust was compared to the similar results reported in the literature and its toxic potency is

  9. A QSAR study of radical scavenging antioxidant activity of a series of flavonoids using DFT based quantum chemical descriptors--the importance of group frontier electron density.

    PubMed

    Sarkar, Ananda; Middya, Tapas Ranjan; Jana, Atish Dipnakar

    2012-06-01

    In a pursuit of electronic level understanding of the antioxidant activity of a series of flavonoids, quantitative structure-activity relationship (QSAR) studies have been carried out using density functional theory (DFT) based quantum chemical descriptors. The best QSAR model have been selected for which the computed square correlation coefficient r(2) = 0.937 and cross-validated squared correlation coefficient q(2) =0.916. The QSAR model indicates that hardness (η), group electrophilic frontier electron density (F(E)(A)) and group philicity (ω(B)(+)) of individual molecules are responsible for in vitro biological activity. To the best our knowledge, the group electrophilic frontier electron density (F(E)(A)) has been used for the first time to explain the radical scavenging activity (RSA) of flavonoids. The excellent correlation between the RSA and the above mentioned DFT based descriptors lead us to predict new antioxidants having very good antioxidant activity.

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

    PubMed Central

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

    2016-01-01

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

  11. A register-based study of the antimicrobial usage in Danish veal calves and young bulls.

    PubMed

    Fertner, Mette; Toft, Nils; Martin, Henrik Læssøe; Boklund, Anette

    2016-09-01

    High antimicrobial usage and multidrug resistance have been reported in veal calves in Europe. This may be attributed to a high risk of disease as veal calves are often purchased from numerous dairy herds, exposed to stress related to the transport and commingling of new animals, and fed a new ration. In this study, we used national register data to characterize the use of antimicrobials registered for large Danish veal calf and young bull producing herds in 2014. A total of 325 herds with veal calf and potentially young bull production were identified from the Danish Cattle database. According to the national Danish database on drugs for veterinary use (VetStat), a total of 537,399 Animal Daily Doses (ADD200) were registered for these 325 herds during 2014. The amount of antimicrobials registered in 2014 varied throughout the year, with the highest amounts registered in autumn and winter. Antimicrobials were registered for respiratory disorders (79%), joints/limbs/CNS disorders (17%), gastrointestinal disorders (3.7%) and other disorders (0.3%). Of the registered antimicrobials, 15% were for oral and 85% for parenteral administration. Long-acting formulations with a therapeutic effect of more than 48h covered 58% of the drugs for parenteral use. Standardized at the herd-level, as ADD200/100 calves/day, antimicrobial use distributed as median [CI95%] for starter herds (n=22): 2.14 [0.19;7.58], finisher herds (n=24): 0.48 [0.00;1.48], full-line herds (n=183): 0.78 [0.05;2.20] and herds with an inconsistent pattern of movements (n=96): 0.62 [0.00;2.24]. Full-line herds are herds, which purchase calves directly from a dairy herd and raise them to slaughter. Furthermore, we performed a risk factor analysis on the 183 herds with a full-line production. Here, we investigated, whether the number of suppliers, the number of calves purchased, the frequency of purchase, the average age at introduction, the average time in the herd and vaccination influenced the amount of

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  14. 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.

  15. QSAR models for Daphnia magna toxicity prediction of benzoxazinone allelochemicals and their transformation products.

    PubMed

    Lo Piparo, Elena; Fratev, Filip; Lemke, Frank; Mazzatorta, Paolo; Smiesko, Martin; Fritz, Jona Ines; Benfenati, Emilio

    2006-02-22

    The overall objective of this study is the ecotoxicological characterization of the benzoxazinone 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA), the benzoxazolinones benzoxazolin-2-one (BOA) and 6-methoxybenzoxazolin-2-one (MBOA), and their transformation products: phenoxazinones 2-acetylamino-7-methoxy-3H-phenoxazin-3-one (AAMPO), 2-acetylamino-3H-phenoxazin-3-one (AAPO), 2-amino-7-methoxy-3H-phenoxazin-3-one (AMPO), and 2-amino-3H-phenoxazin-3-one (APO); aminophenol 2-aminophenol AP); acetamide N-(2-hydroxyphenyl)acetamide (HPAA); and malonamic acid amide N-(2-hydroxyphenyl)malonamic acid (HPMA). A comparison between empirical results and theoretical ones using rules-based prediction of toxicity was done, and it can be concluded that only the degradation metabolites exhibited significant ecotoxic effect. Using synthetic pesticides knowledge, several QSAR models were trained with various approaches and descriptors. The models generated exhibited good internal predictive ability (R(cv)2 > 0.6) and were used to predict the toxicity of the natural compounds studied.

  16. Simulating alpha/beta selectivity at the human thyroid hormone receptor: consensus scoring using multidimensional QSAR.

    PubMed

    Vedani, Angelo; Zumstein, Martin; Lill, Markus A; Ernst, Beat

    2007-01-01

    We present a consensus-scoring study on the human thyroid hormone receptor alpha and beta using two receptor-modeling concepts (software Quasar and Raptor) that are based on multidimensional QSAR and allow for the explicit simulation of induced fit. The binding mode of 82 agonists and indirect antagonists, spanning an activity range of seven orders of magnitude in K(i), was identified through flexible docking to the respective X-ray crystal structures (Yeti software) and represented by a 4D data set with up to four conformations per compound. The receptor surrogates for the thyroid alpha receptor converged at a cross-validated r(2) of 0.846/0.919 (64 training compounds; for Quasar and Raptor, respectively) and yielded a predictive r(2) of 0.812/0.814 (18 test compounds); the models for the thyroid beta receptor resulted in a cross-validated r(2) of 0.823/0.909 and a predictive r(2) of 0.665/0.796, respectively. Consensus was achieved as, on average, the calculated activities of the training set differ only by a factor of 2.2 in K(i) and those of the test set by a factor of 2.8 when predicted by Quasar and Raptor, respectively.

  17. 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.

  18. 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.

  19. Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models

    PubMed Central

    Alexander, D. L. J.; Tropsha, A.; Winkler, David A.

    2015-01-01

    The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This paper clarifies some apparent confusion over the use of the coefficient of determination, R2, as a measure of model fit and predictive power in QSAR and QSPR modelling. R2 (or R2) has been used in various contexts in the literature in conjunction with training and test data, for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha1 in a strict statistical manner. Shortcomings in these criteria are identified and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data, but to guide the application of R2 as a model fit statistic. Examples are used to illustrate both correct and incorrect use of R2. Reporting the root mean squared error or equivalent measures of dispersion, typically of more practical importance than R2, is also encouraged and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined. PMID:26099013

  20. QSARs for photo-induced toxicity of polycyclic aromatic hydrocarbons (PAHs)

    SciTech Connect

    Mekenyan, O.; Call, D.; Ankley, G.; Veith, G.

    1994-12-31

    Photo-induced toxicity of polycyclic aromatic hydrocarbons (PAHs) was found to be a result of competing internal and external factors which interact to produce a complex, multilinear relationship between toxicity and chemical structure. The relationship between molecular electronic structure and photo-dynamic effects was studied in both ground and excited states. A measure of the energy required for an electron to be elevated from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO), or HOMO-LUMO gap, provided a useful ground state index to explain the persistence, light absorption, and eventual photo-induced toxicity of PAHs to Daphnia magna. The derived QSARs clearly distinguished photo-induced toxicity differences between pairs of structurally similar PAHs, such as phenanthrene and anthracene, benzo[a]anthracene and tetracene, etc. Those PAHs exhibiting photo-induced toxicity were consistently within a specific HOMO-LUMO gap range. The relationship between the excited state electronic parameters and toxicity was also studied. Significant correlations were found with the measured energies of singlet and triplet states from spectroscopic data. An investigation of the effect of substituent additions on photo-induced acute toxicity of parent PAHs revealed that alkyl and hydroxy moieties did not significantly reduce the HOMO-LUMO gap of the parent PAHs. Nitro, alkene and chloro substituents cause gap reductions, whereby certain derivatives of parent chemicals that were close to the ``toxic region`` of the electronic gap could become phototoxic with such additions.

  1. Synthesis, algal inhibition activities and QSAR studies of novel gramine compounds containing ester functional groups

    NASA Astrophysics Data System (ADS)

    Li, Xia; Yu, Liangmin; Jiang, Xiaohui; Xia, Shuwei; Zhao, Haizhou

    2009-05-01

    2,5,6-Tribromo-1-methylgramine (TBG), isolated from bryozoan Zoobotryon pellucidum was shown to be very efficient in preventing recruitment of larval settlement. In order to improve the compatibility of TBG and its analogues with other ingredients in antifouling paints, structural modification of TBG was focused mainly on halogen substitution and N-substitution. Two halogen-substitute gramines and their derivatives which contain ester functional groups at N-position of gramines were synthesized. Algal inhibition activities of the synthesized compounds against algae Nitzschia closterium were evaluated and the Median Effective Concentration (EC50) range was 1.06-6.74 μg ml-1. Compounds that had a long chain ester group exhibited extremely high antifouling activity. Quantitive Structure Activity Relationship (QSAR) studies with multiple linear regression analysis were applied to find correlation between different calculated molecular descriptors and biological activity of the synthesized compounds. The results show that the toxicity (log (1/EC50)) is correlated well with the partition coefficient log P. Thus, these products have potential function as antifouling agents.

  2. In Silico Model for Developmental Toxicity: How to Use QSAR Models and Interpret Their Results.

    PubMed

    Marzo, Marco; Roncaglioni, Alessandra; Kulkarni, Sunil; Barton-Maclaren, Tara S; Benfenati, Emilio

    2016-01-01

    Modeling developmental toxicity has been a challenge for (Q)SAR model developers due to the complexity of the endpoint. Recently, some new in silico methods have been developed introducing the possibility to evaluate the integration of existing methods by taking advantage of various modeling perspectives. It is important that the model user is aware of the underlying basis of the different models in general, as well as the considerations and assumptions relative to the specific predictions that are obtained from these different models for the same chemical. The evaluation on the predictions needs to be done on a case-by-case basis, checking the analogs (possibly using structural, physicochemical, and toxicological information); for this purpose, the assessment of the applicability domain of the models provides further confidence in the model prediction. In this chapter, we present some examples illustrating an approach to combine human-based rules and statistical methods to support the prediction of developmental toxicity; we also discuss assumptions and uncertainties of the methodology. PMID:27311466

  3. Development of Dual Inhibitors against Alzheimer's Disease Using Fragment-Based QSAR and Molecular Docking

    PubMed Central

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

    2014-01-01

    Alzheimer's (AD) is the leading cause of dementia among elderly people. Considering the complex heterogeneous etiology of AD, there is an urgent need to develop multitargeted drugs for its suppression. β-amyloid cleavage enzyme (BACE-1) and acetylcholinesterase (AChE), being important for AD progression, have been considered as promising drug targets. In this study, a robust and highly predictive group-based QSAR (GQSAR) model has been developed based on the descriptors calculated for the fragments of 20 1,4-dihydropyridine (DHP) derivatives. A large combinatorial library of DHP analogues was created, the activity of each compound was predicted, and the top compounds were analyzed using refined molecular docking. A detailed interaction analysis was carried out for the top two compounds (EDC and FDC) which showed significant binding affinity for BACE-1 and AChE. This study paves way for consideration of these lead molecules as prospective drugs for the effective dual inhibition of BACE-1 and AChE. The GQSAR model provides site-specific clues about the molecules where certain modifications can result in increased biological activity. This information could be of high value for design and development of multifunctional drugs for combating AD. PMID:25019089

  4. 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.

  5. 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.

  6. Comparison of Cramer classification between Toxtree, the OECD QSAR Toolbox and expert judgment.

    PubMed

    Bhatia, Sneha; Schultz, Terry; Roberts, David; Shen, Jie; Kromidas, Lambros; Marie Api, Anne

    2015-02-01

    The Threshold of Toxicological Concern (TTC) is a pragmatic approach in risk assessment. In the absence of data, it sets up levels of human exposure that are considered to have no appreciable risk to human health. The Cramer decision tree is used extensively to determine these exposure thresholds by categorizing non-carcinogenic chemicals into three different structural classes. Therefore, assigning an accurate Cramer class to a material is a crucial step to preserve the integrity of the risk assessment. In this study the Cramer class of over 1000 fragrance materials across diverse chemical classes were determined by using Toxtree (TT), the OECD QSAR Toolbox (TB), and expert judgment. Disconcordance was observed between TT and the TB. A total of 165 materials (16%) showed different results from the two programs. The overall concordance for Cramer classification between TT and expert judgment is 83%, while the concordance between the TB and expert judgment is 77%. Amines, lactones and heterocycles have the lowest percent agreement with expert judgment for TT and the TB. For amines, the expert judgment agreement is 45% for TT and 55% for the TB. For heterocycles, the expert judgment agreement is 55% for TT and the TB. For lactones, the expert judgment agreement is 56% for TT and 50% for the TB. Additional analyses were conducted to determine the concordance within various chemical classes. Critical checkpoints in the decision tree are identified. Strategies and guidance on determining the Cramer class for various chemical classes are discussed.

  7. Hepatoprotection of sesquiterpenoids: a quantitative structure-activity relationship (QSAR) approach.

    PubMed

    Vinholes, Juliana; Rudnitskaya, Alisa; Gonçalves, Pedro; Martel, Fátima; Coimbra, Manuel A; Rocha, Sílvia M

    2014-03-01

    The relative hepatoprotection effect of fifteen sesquiterpenoids, commonly found in plants and plant-derived foods and beverages was assessed. Endogenous lipid peroxidation (assay A) and induced lipid peroxidation (assay B) were evaluated in liver homogenates from Wistar rats by the thiobarbituric acid reactive species test. Sesquiterpenoids with different chemical structures were tested: trans,trans-farnesol, cis-nerolidol, (-)-α-bisabolol, trans-β-farnesene, germacrene D, α-humulene, β-caryophyllene, isocaryophyllene, (+)-valencene, guaiazulene, (-)-α-cedrene, (+)-aromadendrene, (-)-α-neoclovene, (-)-α-copaene, and (+)-cyclosativene. Ascorbic acid was used as a positive antioxidant control. With the exception of α-humulene, all the sesquiterpenoids under study (1mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The 3D-QSAR models developed, relating the hepatoprotection activity with molecular properties, showed good fit (Radj(2) 0.819 and 0.972 for the assays A and B, respectively) with good prediction power (Q(2)>0.950 and SDEP<2%, for both models A and B). A network of effects associated with structural and chemical features of sesquiterpenoids such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds.

  8. QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

    NASA Astrophysics Data System (ADS)

    Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

    2015-10-01

    Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

  9. QSAR studies on CCR2 antagonists with chiral sensitive hologram descriptors.

    PubMed

    Nair, Pramod C; Srikanth, K; Sobhia, M Elizabeth

    2008-02-15

    Chemokines are small molecular weight water-soluble proteins playing a key role in immunomodulation and host-defense mechanisms. CCR2 receptor is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity, and type 2 diabetes. Reported, herein are the QSAR studies performed on a diverse set of enantiopure analogues reported as CCR2 antagonists by hologram analysis. The best model highlights the importance of chirality feature in comparison with the other models developed without the chirality. The validated model showed high internal and external predictive power. The robustness of the model was achieved with good statistical r(2) of 0.945 and cross-validated r(cv)(2) of 0.837. The challenging test predictivity of the model was confirmed with r(pred)(2) of 0.807. The fragment fingerprints help in understanding essential pharmacophoric features for CCR2 antagonism and provide basis for SAR of the molecules. The 2D contribution maps with fragment information will be useful for the design of novel CCR2 antagonists having improved efficacy.

  10. Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling.

    PubMed

    Mansouri, Kamel; Consonni, Viviana; Durjava, Mojca Kos; Kolar, Boris; Öberg, Tomas; Todeschini, Roberto

    2012-10-01

    Polybrominated diphenyl ethers (PBDEs) are used as flame retardants in textiles, foams and plastics. Highly bioaccumulative with toxic effects including developmental neurotoxicity estrogen and thyroid hormones disruption, they are considered as persistent organic pollutants (POPs) and have been found in human tissues, wildlife and biota worldwide. But only some of them are banned from EU market. For the environmental fate studies of these compounds the bioconcentration factor (BCF) is one of the most important endpoints to start with. We applied quantitative structure-activity relationships techniques to overcome the limited experimental data and avoid more animal testing. The aim of this work was to assess the bioaccumulation of PBDEs by means of QSAR. First, a BCF dataset of specifically conducted experiments was modeled. Then the study was extended by predicting the bioaccumulation and biomagnification factors using some experimental values from the literature. Molecular descriptors were calculated using DRAGON 6. The most relevant ones were selected and resulting models were compared paying attention to the applicability domain.

  11. 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. PMID:25010341

  12. 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

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

    PubMed Central

    Fjodorova, Natalja; Novič, Marjana

    2012-01-01

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

  14. The kinetics and QSAR of abiotic reduction of mononitro aromatic compounds catalyzed by activated carbon.

    PubMed

    Gong, Wenwen; Liu, Xinhui; Gao, Ding; Yu, Yanjun; Fu, Wenjun; Cheng, Dengmiao; Cui, Baoshan; Bai, Junhong

    2015-01-01

    The kinetics of abiotic reduction of mono-nitro aromatic compounds (mono-NACs) catalyzed by activated carbon (AC) in an anaerobic system were examined. There were 6 types of substituent groups on nitrobenzene, including methyl, chlorine, amino, carboxyl, hydroxyl and cyanogen groups, at the ortho, meta or para positions. Our results showed that reduction followed pseudo-first order reaction kinetics, and that the rate constant (logkSA) varied widely, ranging between -4.77 and -2.82, depending upon the type and position of the substituent. A quantitative structure-activity relationship (QSAR) model using 15 theoretical molecular descriptors and partial-least-squares (PLS) regression was developed for the reduction rates of mono-NACs catalyzed by AC. The cross-validated regression coefficient (Qcum(2), 0.861) and correlation coefficient (R(2), 0.898) indicated significantly high robustness of the model. The VIP (variable importance in the projection) values of energy of the lowest unoccupied molecular orbital (ELUMO) and the maximum net atomic charge on the aromatic carbon bound to the nitro group (QC(-)) were 1.15 and 1.01, respectively. These values indicated that the molecular orbital energies and the atomic net charges might play important roles in the reduction of mono-NACs catalyzed by AC in anaerobic systems.

  15. A New Approach to Radial Basis Function Approximation and Its Application to QSAR

    PubMed Central

    2015-01-01

    We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor’s contribution. Linear radial basis functions allow one to achieve more accurate predictions for diverse data sets. Taking into account the contribution of each descriptor produces more accurate similarity values used for model development. The method was validated on 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. We also compared the new method with five different QSAR methods implemented in the EPA T.E.S.T. program. Our approach, implemented in the program GUSAR, showed a reasonable accuracy of prediction and high coverage for all external test sets, providing more accurate prediction results than the comparison methods and even the consensus of these methods. Using our new method, we have created models for physicochemical and toxicity endpoints, which we have made freely available in the form of an online service at http://cactus.nci.nih.gov/chemical/apps/cap. PMID:24451033

  16. Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols.

    PubMed

    Mondal, Chanchal; Halder, Amit Kumar; Adhikari, Nilanjan; Jha, Tarun

    2013-10-01

    Cholesteryl ester transfer protein (CETP) converts high density lipoprotein cholesterol to low density lipoproteins. It is a promising target for treatment of coronary heart disease. Two dimensional quantitative structure activity relationship (2D-QSAR), hologram QSAR (HQSAR) studies and comparative molecular field analysis (CoMFA) as well as comparative molecular similarity analysis (CoMSIA) were performed on 104 CETP inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., q(2) and R(2)pred respectively. The best 2D-QSAR model was obtained with q(2) and R(2)pred values of 0.794 and 0.796 respectively. The 2D-QSAR study suggests that unsaturation, branching and van der Waals volumes may play important roles. The HQSAR model showed q(2) and R(2)pred values of 0.628 and 0.550 respectively. Similarly, CoMFA model showed q(2) and R(2)pred values of 0.707 and 0.755 respectively whereas CoMSIA model was obtained with q(2) and R(2)pred values of 0.696 and 0.703 respectively. CoMFA and CoMSIA studies indicate that steric factors are important at substituted phenoxy and tetrafluoroethoxy groups whereas electropositive factors play important role at difluoromethyl group. The results of 3D-QSAR studies validate those of 2D-QSAR and HQSAR studies as well as the earlier observed SAR data. Current work may help to develop better CETP inhibitors.

  17. 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. PMID:15669693

  18. Hazard Analysis Database Report

    SciTech Connect

    GRAMS, W.H.

    2000-12-28

    The Hazard Analysis Database was developed in conjunction with the hazard analysis activities conducted in accordance with DOE-STD-3009-94, Preparation Guide for U S . Department of Energy Nonreactor Nuclear Facility Safety Analysis Reports, for HNF-SD-WM-SAR-067, Tank Farms Final Safety Analysis Report (FSAR). The FSAR is part of the approved Authorization Basis (AB) for the River Protection Project (RPP). This document describes, identifies, and defines the contents and structure of the Tank Farms FSAR Hazard Analysis Database and documents the configuration control changes made to the database. The Hazard Analysis Database contains the collection of information generated during the initial hazard evaluations and the subsequent hazard and accident analysis activities. The Hazard Analysis Database supports the preparation of Chapters 3 ,4 , and 5 of the Tank Farms FSAR and the Unreviewed Safety Question (USQ) process and consists of two major, interrelated data sets: (1) Hazard Analysis Database: Data from the results of the hazard evaluations, and (2) Hazard Topography Database: Data from the system familiarization and hazard identification.

  19. Hazard Analysis Database Report

    SciTech Connect

    GAULT, G.W.

    1999-10-13

    The Hazard Analysis Database was developed in conjunction with the hazard analysis activities conducted in accordance with DOE-STD-3009-94, Preparation Guide for US Department of Energy Nonreactor Nuclear Facility Safety Analysis Reports, for the Tank Waste Remediation System (TWRS) Final Safety Analysis Report (FSAR). The FSAR is part of the approved TWRS Authorization Basis (AB). This document describes, identifies, and defines the contents and structure of the TWRS FSAR Hazard Analysis Database and documents the configuration control changes made to the database. The TWRS Hazard Analysis Database contains the collection of information generated during the initial hazard evaluations and the subsequent hazard and accident analysis activities. The database supports the preparation of Chapters 3,4, and 5 of the TWRS FSAR and the USQ process and consists of two major, interrelated data sets: (1) Hazard Evaluation Database--Data from the results of the hazard evaluations; and (2) Hazard Topography Database--Data from the system familiarization and hazard identification.

  20. Database for propagation models

    NASA Technical Reports Server (NTRS)

    Kantak, Anil V.

    1991-01-01

    A propagation researcher or a systems engineer who intends to use the results of a propagation experiment is generally faced with various database tasks such as the selection of the computer software, the hardware, and the writing of the programs to pass the data through the models of interest. This task is repeated every time a new experiment is conducted or the same experiment is carried out at a different location generating different data. Thus the users of this data have to spend a considerable portion of their time learning how to implement the computer hardware and the software towards the desired end. This situation may be facilitated considerably if an easily accessible propagation database is created that has all the accepted (standardized) propagation phenomena models approved by the propagation research community. Also, the handling of data will become easier for the user. Such a database construction can only stimulate the growth of the propagation research it if is available to all the researchers, so that the results of the experiment conducted by one researcher can be examined independently by another, without different hardware and software being used. The database may be made flexible so that the researchers need not be confined only to the contents of the database. Another way in which the database may help the researchers is by the fact that they will not have to document the software and hardware tools used in their research since the propagation research community will know the database already. The following sections show a possible database construction, as well as properties of the database for the propagation research.