RT Journal Article
SR Electronic
T1 Cheminformatics tools for analyzing and designing optimized small molecule libraries
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 358978
DO 10.1101/358978
A1 Moret, Nienke
A1 Clark, Nicholas A.
A1 Hafner, Marc
A1 Wang, Yuan
A1 Lounkine, Eugen
A1 Medvedovic, Mario
A1 Wang, Jinhua
A1 Gray, Nathanael
A1 Jenkins, Jeremy
A1 Sorger, Peter K.
YR 2018
UL http://biorxiv.org/content/early/2018/06/29/358978.abstract
AB Libraries of highly 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 such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. 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 and public libraries can be progressively improved using the latest data.