Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding

Journal of Medicinal Chemistry
Kevin McCloskeyPatrick Riley

Abstract

DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from large libraries of commercial and easily synthesizable compounds. We train models using only DEL selection data and apply automated or automatable filters to the predictions. We perform a large prospective study (∼2000 compounds) across three diverse protein targets: sEH (a hydrolase), ERα (a nuclear receptor), and c-KIT (a kinase). The approach is effective, with an overall hit rate of ∼30% at 30 μM and discovery of potent compounds (IC50 < 10 nM) for every target. The system makes useful predictions even for molecules dissimilar to the original DEL, and the compounds identified are diverse, predominantly drug-like, and different from known ligands. This work demonstrates a powerful new approach to hit-finding.

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Citations

Mar 20, 2021·ACS Medicinal Chemistry Letters·Christopher A ReiherScott E Wolkenberg
Apr 24, 2021·Nature Communications·Ali BashirB Scott Ferguson
Apr 24, 2021·Cell Chemical Biology·Predrag JevtićMichael Rapé
Apr 24, 2021·Chembiochem : a European Journal of Chemical Biology·Yiran Huang, Xiaoyu Li
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Jul 18, 2021·Journal of Computer-aided Molecular Design·Eelke B Lenselink, Pieter F W Stouten
Aug 24, 2021·ACS Pharmacology & Translational Science·Adrián Gironda-MartínezDario Neri
Nov 20, 2021·Pest Management Science·Stephen O Duke, Franck E Dayan
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Dec 30, 2021·Molecular Informatics·Peter ErtlPaolo Tosco
Feb 6, 2022·Nature Chemistry·Yiran HuangXiaoyu Li

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