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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1023] [Impact Index Per Article: 102.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Solimeo R, Zhang J, Kim M, Sedykh A, Zhu H. Predicting chemical ocular toxicity using a combinatorial QSAR approach. Chem Res Toxicol 2012; 25:2763-9. [PMID: 23148656 DOI: 10.1021/tx300393v] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Regulatory agencies require testing of chemicals and products to protect workers and consumers from potential eye injury hazards. Animal screening, such as the rabbit Draize test, for potential environmental toxicants is time-consuming and costly. Therefore, virtual screening using computational models to tag potential ocular toxicants is attractive to toxicologists and policy makers. We have developed quantitative structure-activity relationship (QSAR) models for a set of small molecules with animal ocular toxicity data compiled by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods. The data set was initially curated by removing duplicates, mixtures, and inorganics. The remaining 75 compounds were used to develop QSAR models. We applied both k nearest neighbor and random forest statistical approaches in combination with Dragon and Molecular Operating Environment descriptors. Developed models were validated on an external set of 34 compounds collected from additional sources. The external correct classification rates (CCR) of all individual models were between 72 and 87%. Furthermore, the consensus model, based on the prediction average of individual models, showed additional improvement (CCR = 0.93). The validated models could be used to screen external chemical libraries and prioritize chemicals for in vivo screening as potential ocular toxicants.
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Affiliation(s)
- Renee Solimeo
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
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