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    Perspective

    QSAR Modeling: Where Have You Been? Where Are You Going To?
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    † Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, V6H3Z6, Canada
    ‡ Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
    § Department of Molecular Structure and Cheminformatics, A. V. Bogatsky Physical-Chemical Institute, National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
    ∥ Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
    ⊥ Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
    # School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, U.K.
    ∞ Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
    × Martin Consulting, Waukegan, Illinois 60079, United States
    ○ Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
    ● Tripos, Inc., St. Louis, Missouri 63144, United States
    ◇ Environment and Health Department, Istituto Superiore di Sanità, Rome, 00161, Italy
    ◆ Altamira LLC, Columbus, Ohio 43235, United States
    △ Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43215, United States
    Molecular Networks GmbH, 91052 Erlangen, Germany
    ∀ National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27519, United States
    *E-mail: [email protected]. Phone: +19199662955.
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    Journal of Medicinal Chemistry

    Cite this: J. Med. Chem. 2014, 57, 12, 4977–5010
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    https://doi.org/10.1021/jm4004285
    Published December 18, 2013
    Copyright © 2013 American Chemical Society
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    Abstract

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    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.

    Copyright © 2013 American Chemical Society

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    Journal of Medicinal Chemistry

    Cite this: J. Med. Chem. 2014, 57, 12, 4977–5010
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    https://doi.org/10.1021/jm4004285
    Published December 18, 2013
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