Is QSAR relevant to drug discovery?
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Updated: Jun 27, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
1Bristol-Myers Squibb Co, Research and Development, Computer-Aided Drug Design, PO Box 4000, Princeton, NJ 08543, USA. arthur.doweyko@bms.com
Abstract:
The concept of quantitative SAR (QSAR) is inherently imbued with an expectation of predictivity, novel insights and the generation of useful hypotheses, particularly as applied to the drug discovery process. However, even recently developed QSAR models often appear to be flawed, characterized by mediocre predictive power and undecipherable descriptors. As a result, users may be able to derive only a vague notion of which molecular features are correlated to activity. Consideration of several precautions is necessary to attempt to circumvent the misuse and misunderstanding of the QSAR technique. Issues related with QSAR include an erroneous association of correlation with causation, the close relationship between large numbers of descriptors and the effect of chance factor, the misuse of the 'leave-one-out' paradigm, and finally, the QSAR enigma, wherein the predictivity of a model is not necessarily a measure of a model's utility.
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