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
Current Medicinal Chemistry
Title: How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review
Volume: 16 Issue: 32
Author(s): T. Scior, J. L. Medina-Franco, Q.-T. Do, K. Martinez-Mayorga, J. A. Yunes Rojas and P. Bernard
Affiliation:
Keywords: Chemoinformatics, quantitative structure-activity relationships, problems, solutions, regression analysis, virtual screening
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.
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Scior T., Medina-Franco L. J., Do Q.-T., Martinez-Mayorga K., Yunes Rojas A. J. and Bernard P., How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review , Current Medicinal Chemistry 2009; 16 (32) . https://dx.doi.org/10.2174/092986709789578213
DOI https://dx.doi.org/10.2174/092986709789578213 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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