Cheminformatics approaches to analyze diversity in compound screening libraries

Abstract

As high-throughput screening matures as a discipline, cheminformatics is playing an increasingly important role in selecting new compounds for diverse screening libraries. New visualization techniques such as multi-fusion similarity maps, scaffold trees, and principal moments of inertia plots provide complementary information on compound libraries and enable identification of unexplored regions of chemical space with potential biological relevance. Quantitative metrics have been developed to analyze libraries for properties such as natural product-likeness and shape complexity. Analysis of high-throughput screening results and drug discovery programs identify compounds problematic for screening. Taken together these approaches allow us to increase the diversity of biological outcomes available in compound screening libraries and improve the success rates of high-throughput screening against new targets without making significant increases in the size of compound libraries.