Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning

Abstract

Combinatorial chemistry and high-throughput screening are revolutionizing the process of lead discovery in the pharmaceutical industry. Large numbers of structures and vast quantities of biological assay data are quickly being accumulated, overwhelming traditional structure/activity relationship (SAR) analysis technologies. Recursive partitioning is a method for statistically determining rules that classify objects into similar categories or, in this case, structures into groups of molecules with similar potencies. SCAM is a computer program implemented to make extremely efficient use of this methodology. Depending on the size of the data set, rules explaining biological data can be determined interactively. An example data set of 1650 monoamine oxidase inhibitors exemplifies the method, yielding substructural rules and leading to general classifications of these inhibitors. The method scales linearly with the number of descriptors, so hundreds of thousands of structures can be analyzed utilizing thousands to millions of molecular descriptors. There are currently no methods to deal with statistical analysis problems of this size. An important aspect of this analysis is the ability to deal with mixtures, i.e., identify SAR rules for classes of compounds in the same data set that might be binding in different ways. Most current quantitative structure/activity relationship methods require that the compounds follow a single mechanism. Advantages and limitations of this methodology are presented.