GuideDock: Geometry-Guided Diffusion for Rigid Protein-Protein Docking

Modeling 3D protein-protein interactions is central to structural biology and the design of drugs and therapeutics. Recent deep learning methods for rigid binder pose prediction can be more efficient than classical docking software and expensive co-folding models. However, these methods typically operate directly in $\mathrm{SE}(3)$ pose space with limited explicit priors over interface geometry. As a result, they must jointly learn which binder-receptor regions are compatible and which rigid transformations realize that compatibility, often leading to unreliable sampling and suboptimal accuracy. In this work, we introduce GuideDock, a framework that guides diffusion-based pose generation with distance maps recovered by latent flow matching. GuideDock treats recovered distance maps as soft geometric constraints that steer the diffusion process toward physically plausible rigid poses. Across DIPS-Het and Docking Benchmark 5.5, GuideDock outperforms competing deep learning-based rigid docking baselines, improving mean DockQ by 30% and reducing mean complex RMSD by up to 15% with only a single generated pose, while running up to 300 times faster than the best co-folding models.