A robust clustering method for chemical structures

Abstract

A clustering method based on finding the largest set of disconnected fragments that two chemical compounds have in common is shown to be able to group structures in a way that is ideally suited to medicinal chemistry programs. We describe how markedly improved results can be obtained by using a similarity metric that accounts not just for the size of the shared fragments but also on their relative arrangement in the two parent compounds. The use of a physiochemical atom typing scheme is also shown to provide significant contributions. Results from calculations using a test set consisting of actives from nine different important biological target proteins demonstrate the strengths of our clustering method and the advantages over other approaches that are widely used throughout the pharmaceutical industry.