Recursive Partitioning Analysis of a Large Structure−Activity Data Set Using Three-Dimensional Descriptors

Abstract

Large chemical data sets are becoming available from high throughput screening of corporate collections and chemical libraries. There is a growing need to develop three-dimensional pharmacophores from these large data sets to guide database screening, chemical library design, and lead optimization. Recursive partitioning (RP) is a statistical method that can be used to analyze very large data sets; data sets of over 100 000 observations and over 2 000 000 descriptors pose no computational problems. Our idea is to encode the three-dimensional features of chemical compounds into bit strings and use RP to determine the important features that statistically correlate to the biological activities of these compounds. This kind of structure−activity relationship analysis (SAR) can be considered as the first step to the goal of pharmacophore identification for large chemical data sets. We report here our RP work that for the first time successfully retrieved 3D SARs from a large, heterogeneous data set of 1650 monoamine oxidase (MAO) inhibitors, which indicates the feasibility of 3D analysis of a few thousand compounds.