QSAR Modeling: Where Have You Been? Where Are You Going To?

Artem Cherkasov(University of British Columbia), Eugene Muratov(University of North Carolina at Chapel Hill), Denis Fourches(University of North Carolina at Chapel Hill), Alexandre Varnek(Université de Strasbourg), Igor I. Baskin(Lomonosov Moscow State University), M Cronin(Liverpool John Moores University), John C. Dearden(Liverpool John Moores University), Paola Gramatica(University of Insubria), Yvonne C. Martin, Roberto Todeschini(University of Milano-Bicocca), Viviana Consonni(University of Milano-Bicocca), V. Е. Kuz’min(National Academy of Sciences of Ukraine), Richard D. Cramer, Romualdo Benigni(Istituto Superiore di Sanità), Chihae Yang, James F. Rathman(The Ohio State University), Lothar Terfloth(Molecular Networks (Germany)), Johann Gasteiger(Molecular Networks (Germany)), Ann Richard(Environmental Protection Agency), Alexander Tropsha(University of North Carolina at Chapel Hill)
Journal of Medicinal Chemistry
December 18, 2013
Cited by 2,036

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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