Drug-likeness is a frequently invoked, although not always precisely defined, concept in drug discovery. Opinions on drug-likeness are to a large extent shaped by the relationships that are observed between surrogate measures of drug-likeness (e.g. aqueous solubility; permeability; pharmacological promiscuity) and fundamental physicochemical properties (e.g. lipophilicity; molecular size). This article draws on examples from the literature to highlight approaches to data analysis that exaggerate trends in data and the term correlation inflation is introduced in the context of drug discovery. Averaging groups of data points prior to analysis is a common cause of correlation inflation and results from analysis of binned continuous data should always be treated with caution.
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We thank Anthony Nicholls for valuable advice and the reviewers of the manuscript for their helpful and constructive feedback. We are grateful to Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Pesquisa (CNPq) for financial support and OpenEye Scientific Software for an academic software license.
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Kenny, P.W., Montanari, C.A. Inflation of correlation in the pursuit of drug-likeness. J Comput Aided Mol Des 27, 1–13 (2013). https://doi.org/10.1007/s10822-012-9631-5