Enabling Open Machine Learning of Deoxyribonucleic Acid-Encoded Library Selections to Accelerate the Discovery of Small Molecule Protein Binders

Machine learning
(ML) is increasingly used in DNA-encoded library
(DEL) screening for ligand discovery, but its success depends on access
to suitable data sets, which are often proprietary and costly. To
overcome this, we present the first fully open, automated DEL-ML framework
using public DEL data sets and chemical fingerprints to enable reproducible,
accessible drug discovery. Our workflowfrom model training
to virtual screening and compound selectionrequires no human
intervention. As a proof of concept, we identified binders for WDR91
by training ML models on the HitGen OpenDEL library (3B molecules)
and screening the Enamine REAL Space library (37B molecules), yielding
50 candidates. Experimental testing confirmed seven novel binders
with dissociation constants between 2.7–21 μM. Our open-source
approach matches the performance of proprietary methods, demonstrating
that public DEL data can support robust ML-driven ligand discovery
and fostering transparency and broader community participation in
drug development.