Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries. Issue 5 (16th May 2019)

• Record Type: Journal Article
• Authors: Moret, Nienke; Clark, Nicholas A.; Hafner, Marc; Wang, Yuan; Lounkine, Eugen; Medvedovic, Mario; Wang, Jinhua; Gray, Nathanael; Jenkins, Jeremy; Sorger, Peter K.
• Is Part Of: Cell chemical biology. Volume 26:Issue 5(2019)
• Page Start: 765
• Page End: 777.e3
• Publication Date: 2019-05-16
• Subjects: chemical library -- mechanism of action -- kinase inhibitors -- cheminformatics -- small molecule -- drug discovery -- drug repurposing -- chemical biology -- chemical genetics -- machine learning; Biochemistry -- Periodicals; 572.05
• DOI: 10.1016/j.chembiol.2019.02.018
• Languages: English
• ISSNs: 2451-9456

Abstract

Summary: Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online toolhttp://www.smallmoleculesuite.org, assembles sets of compounds with the lowest possible off-target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them and led us to design a new LSP-OptimalKinase library that outperforms existing collections in target coverage and compact size. We also describe a mechanism of action library that optimally covers 1, 852 targets in the liganded genome. Our tools facilitate creation, analysis, and updates of both private and public compound collections. Graphical Abstract: Highlights: Existing small-molecule collections vary greatly on selectivity and target coverage A data-driven approach to library design enhances diversity and library performance The LSP-OptimalKinase library enhances selectivity and coverage for kinome targets The LSP-MoA library optimally targets 1, 852 genes in the liganded genome Abstract : Despite the widespread use of small-molecule libraries, few data-driven tools exist to analyze existing libraries and design new ones. Here, Moret et al. use data on binding selectivity, target coverage, induced cellular phenotype, chemical structure, and phase of clinical development to analyze and design small-molecule libraries.