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Machine learning on DNA-encoded libraries: A new paradigm for hit-finding

Eric A. Sigel
Steven Kearnes
Ling Xue
Xia Tian
Dennis Moccia
Diana Gikunju
Sana Bazzaz
Betty Chan
Matthew A. Clark
John W. Cuozzo
Marie-Aude Guié
John P. Guilinger
Christelle Huguet
Christopher D. Hupp
Anthony D. Keefe
Christopher J. Mulhern
Ying Zhang
Patrick Francis Riley
Journal of Medicinal Chemistry (2020)


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.