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. 2014 Jun 26;57(12):4977-5010.
doi: 10.1021/jm4004285. Epub 2014 Jan 6.

QSAR modeling: where have you been? Where are you going to?

Artem Cherkasov  1 , Eugene N Muratov, Denis Fourches, Alexandre Varnek, Igor I Baskin, Mark Cronin, John Dearden, Paola Gramatica, Yvonne C Martin, Roberto Todeschini, Viviana Consonni, Victor E Kuz'min, Richard Cramer, Romualdo Benigni, Chihae Yang, James Rathman, Lothar Terfloth, Johann Gasteiger, Ann Richard, Alexander Tropsha
Affiliations

QSAR modeling: where have you been? Where are you going to?

Artem Cherkasov et al. J Med Chem. .

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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Figures

Figure 1
Figure 1
The growth of QSAR modeling is caused by the growth of experimental data. Chart is generated by Google Ngram Viewer (http://books.google.com/ngrams); Y-axis – percentage among all books in the Google Ngram database, X-axis – years.
Figure 2
Figure 2
Workflow for predictive QSAR modeling (A) incorporating a critical step of data curation (within the dotted rectangle) that relies on its own special workflow (B).
Figure 3
Figure 3
Estimation of terminal (a) and central (b) fragment's contributions to activity. PQSAR – contribution estimated by developed QSAR model; P' – contribution estimated by the universal interpretation approach.
Figure 4
Figure 4
Overall study design of a QSAR-guided drug discovery project.
Figure 5
Figure 5
General workflow for screening chemical libraries using empirical and QSAR-based filtering.
Figure 6
Figure 6
Adaptive drug design for computer-aided generation of compounds with controlled polypharmacology (see earlier work).
Figure 7
Figure 7
Generation of simplex descriptors for mixtures.
See this image and copyright information in PMC

References

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