Grupo de Estudos em Química Medicinal de Produtos Naturais, NEQUIMED-PN, Instituto de Química de São Carlos, Universidade de São Paulo, Av. Trabalhador Sancarlense 400, São Carlos, SP 13560-970, Brazil.
J Comput Aided Mol Des. 2013 Jan;27(1):1-13. doi: 10.1007/s10822-012-9631-5. Epub 2013 Jan 10.
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.
类药性是药物发现中经常提到的一个概念，尽管它的定义并不总是很准确。类药性的观点在很大程度上受到类药性替代指标（如水溶性、渗透性、药理学混杂性）与基本物理化学性质（如亲脂性、分子大小）之间观察到的关系的影响。本文通过文献中的例子，强调了数据分析方法中夸大数据趋势的问题，并在药物发现的背景下引入了“相关性膨胀”一词。在分析之前对数据点进行平均是导致相关性膨胀的常见原因，因此，对于分箱连续数据的分析结果应始终谨慎处理。
