  DNA-encoded small molecule libraries (DELs) have enabled discovery of novel
inhibitors for many distinct protein targets of therapeutic value through
screening of libraries with up to billions of unique small molecules. We
demonstrate a new approach applying machine learning to DEL selection data by
identifying active molecules from a large commercial collection and a virtual
library of easily synthesizable compounds. We train models using only DEL
selection data and apply automated or automatable filters with chemist review
restricted to the removal of molecules with potential for instability or
reactivity. We validate this approach with a large prospective study (nearly
2000 compounds tested) across three diverse protein targets: sEH (a hydrolase),
ER{\alpha} (a nuclear receptor), and c-KIT (a kinase). The approach is
effective, with an overall hit rate of {\sim}30% at 30 {\textmu}M and discovery
of potent compounds (IC50 <10 nM) for every target. The model 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. Collectively, the quality and quantity of DEL selection data; the
power of modern machine learning methods; and access to large, inexpensive,
commercially-available libraries creates a powerful new approach for hit
finding.
