How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review

T.      Scior; J.   L.   Medina-Franco; Q.-T.      Do; K.      Martinez-Mayorga; J.   A.   Yunes Rojas; P.      Bernard

Abstract

Quantitative Structure-Activity Relationships (QSAR) are based on the hypothesis that changes in molecular structure reflect proportional changes in the observed response or biological activity. In order to successfully conduct QSAR studies certain conditions have to be met that are not frequently reported in the literature. This suggests that some authors are not aware of the principle flaws, occasional shortcomings, and circumstantial downsides of QSAR methods. The present paper focuses on prerequisites to set up correct models and on limitations of model applications. Their implications are systematically described and illustrated as pitfalls that have strong implications in QSAR, and possible solutions are suggested. The paper is focused on small scale 2D- and 3D-QSAR studies for lead optimization. The work is enriched with comprehensive comments and non-mathematical explanations for the computer practitioner in Medicinal Chemistry.

Keywords: Chemoinformatics, quantitative structure-activity relationships, problems, solutions, regression analysis, virtual screening

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