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

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

Journal of medicinal chemistry (2020-06-12)
Kevin McCloskey, 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 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.

MATERIALS
Product Number
Brand
Product Description

Sigma-Aldrich
Magnesium acetate solution, BioUltra, for molecular biology, ~1 M in H2O
Millipore
HIS-Select® HF Nickel Affinity Gel