Literature DB >> 30956147

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

Nienke Moret1, Nicholas A Clark2, Marc Hafner1, Yuan Wang3, Eugen Lounkine3, Mario Medvedovic2, Jinhua Wang4, Nathanael Gray4, Jeremy Jenkins3, Peter K Sorger5.   

Abstract

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 tool http://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.
Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  chemical biology; chemical genetics; chemical library; cheminformatics; drug discovery; drug repurposing; kinase inhibitors; machine learning; mechanism of action; small molecule

Mesh:

Substances:

Year:  2019        PMID: 30956147      PMCID: PMC6526536          DOI: 10.1016/j.chembiol.2019.02.018

Source DB:  PubMed          Journal:  Cell Chem Biol        ISSN: 2451-9448            Impact factor:   8.116


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