DiffDock-Glide: a hybrid physics-based and data-driven approach to molecular docking

Abstract Recent years have seen a rise in applications of deep learning to problems in the molecular sciences. Among them, the diffusion model DiffDock stands out as a method for docking small molecules into protein binding sites. But DiffDock struggles to compete with conventional docking methods, especially for targets outside its training set. We develop a hybrid model called DiffDock-Glide which addresses some shortcomings of deep learning docking methods: it uses a modified generative process to generate samples within a binding pocket and the confidence model is replaced with Glide’s post-docking minimization pipeline. We evaluate DiffDock-Glide on the Posebusters dataset and show improved sampling of near-native poses, especially for sequences without homologues in the training set. We also evaluate DiffDock-Glide’s performance in virtual screening compounds from the DUD-E dataset against receptor structures generated by AlphaFold2 and report enrichment values that broadly surpass those from traditional Glide. Competing Interest Statement Work was completed while Lukas Herron was an intern at Schrodinger Inc.