Journal of Computer-Aided Molecular Design

, Volume 27, Issue 1, pp 1–13

Inflation of correlation in the pursuit of drug-likeness

Authors

    • Grupo de Estudos em Química Medicinal de Produtos Naturais, NEQUIMED-PN, Instituto de Química de São CarlosUniversidade de São Paulo
  • Carlos A. Montanari
    • Grupo de Estudos em Química Medicinal de Produtos Naturais, NEQUIMED-PN, Instituto de Química de São CarlosUniversidade de São Paulo
Perspective

DOI: 10.1007/s10822-012-9631-5

Cite this article as:
Kenny, P.W. & Montanari, C.A. J Comput Aided Mol Des (2013) 27: 1. doi:10.1007/s10822-012-9631-5

Abstract

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.

Keywords

ADMET Correlation Drug-likeness Lipophilicity Solubility Promiscuity

Supplementary material

10822_2012_9631_MOESM1_ESM.txt (626 kb)
Supplementary material 1 (TXT 626 kb)

Copyright information

© Springer Science+Business Media Dordrecht 2013