Drug-motif-based diverse monomer selection: Method and application in combinatorial chemistry

Abstract

This article describes a strategy to explore monomer diversity while incorporating drug motif knowledge into the design and selection of monomers for combinatorial chemistry. The process involves collecting from available electronic databases all those molecules that potentially could be monomers. In this manner we have assembled five DAYLIGHT databases, each containing one of the common functional groups: carboxylic acids, aldehydes, nitriles, primary amines, and secondary amines. The molecules in the databases are then subjected to fingerprint and cluster analysis using the Jarvis-Patrick algorithm and profiles of the compounds are calculated relating to molecular weight, H-bond counts, and rotatable bond flexibility. The cluster information and profiles of the molecules are stored back into the databases for similarity and diversity searches, and for profile prescreening of monomers. To apply drug motif knowledge to a selection an application to an aldehyde set is discussed, in which representatives of each cluster in the aldehyde database are compared with drug molecules in the Standard Derwent File (SDF) in one of three ways to select drug motif-based monomers for purchase or synthesis.