Discovery of Covalent Ligands with AlphaFold3

Covalent inhibitors are a prominent modality for research
and therapeutic
tools. However, a scarcity of computational methods for their discovery
slows progress in this field. AI models such as AlphaFold3 (AF3) have
shown accuracy in ligand pose prediction, but their applicability
for virtual screening campaigns was not assessed. We show that AF3
cofolding predictions and an associated predicted confidence metric
ranks true covalent binders with near-optimal classification over
property-matched decoys, significantly outperforming state-of-the-art
covalent docking tools for a set of protein kinases. In a prospective
virtual screening campaign against the model kinase BTK, we discovered
a chemically distinct, novel, covalent small molecule that displays
potent inhibition in vitro and in cells while maintaining
marked kinome and proteomic selectivity. Co-crystallography validated
the subangstrom accuracy of the predicted AF3 binding mode. These
results demonstrate that AF3 can be practically used to discover novel
chemical matter for kinases, one of the most prolific families of
drug targets.