Cheminformatics Tools for Analyzing and Designing Optimized Small Molecule Collections and Libraries

Abstract

Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe an approach to scoring and creating libraries based on available data, which is often incomplete, on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure, stage of clinical development and user preference. The approach, implemented as R software and a Web-accessible tool also assembles small sets of compounds (typically 2-8) that target a protein of interest while having the lowest possible off target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections, public libraries can be updated based on the latest data and the confounding effects of off-target activities minimized in chemical genetic studies.