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

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.