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 structureactivity 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.

Computational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5)

“…Comparative molecular fields analysis and pharmacophore approaches have been used to model P450 enzymes involved in drug metabolism and which have a role in important drug-drug interactions (de Groot et al, 1996(de Groot et al, , 1999aJones et al, 1996;Ekins et al, 2001). Recursive partitioning methods have been used extensively with large sets of molecules and either continuous (Chen et al, 1998(Chen et al, , 1999 or binary data for therapeutic target endpoints and P450 inhibition (Ekins et al, 2003a) and toxicity properties such as Ames mutagenicity status (Young et al, 2002). Linear regression is perhaps one of the earliest approaches used for QSAR of P450 (Hansch and Zhang, 1993).…”

Similarity-based virtual screening using 2D fingerprints

“…Thus, if the known actives have some degree of structural commonality then a pharmacophore mapping program [69] can be used to identify the common pharmacophore and then to use this pattern as the basis for a 3D database search [70]. Alternatively, if many structurally diverse actives and structurally diverse inactives are available then techniques such as substructural analysis [71], recursive partitioning [72] or binary kernel discrimination [73] should be used. However, when just a few, structurally diverse actives are available then the group fusion approach described here would seem to provide a useful addition to the computational tools available for compound selection.…”

Classification of Kinase Inhibitors Using a Bayesian Model

“…2325 Stanley Young and colleagues implemented a recursive partitioning method a few years ago that can be used to derive predictive models to distinguish active from inactive compounds. 20,21 This method, compared to some older implementations, has the advantage of being able to handle a large number of descriptors and very large compound data sets.…”