SE(3) Equivariant Topologies for Structure-based Drug Discovery

Modeling protein-ligand interactions is a challenging task that has been approached through an array of perspectives. From physics-based computational approaches to vast deep learning pipelines, in silico methods hold promise in reducing experimental overhead in the otherwise tedious and costly drug discovery campaigns. We introduce Protein-Ligand Equivariant Transformer (ProLET), a generalizable model built upon chemically inspired SE(3) equivariant geometric deep learning. We evaluate ProLET on a wide range of established standards, including the notoriously difficult PoseBusters and Merck’s FEP benchmarks, consistently demonstrating superior performance in binding affinity prediction and pose estimation. We demonstrate its effectiveness across different stages in drug discovery, showing that ProLET can be used for lead optimization and hit identification as well as for prioritizing compounds that are selective towards a desired target. By bridging the gap between accuracy, efficiency, and generalizability, ProLET stands as a powerful and adaptive resource, signifying a step towards safe and reliable AI-driven drug discovery.