Unified Sampling and Ranking for Protein Docking With DFMDock

ABSTRACT Recent diffusion‐based approaches to protein–protein docking typically decouple structure generation from decoy ranking. We introduce DFMDock (Denoising Force Matching for Docking), a unified diffusion model that integrates generative sampling and energy‐based ranking through physically motivated supervision. DFMDock predicts both denoising forces and a scalar energy, trained using force matching and energy contrastive objectives. The predicted forces guide the reverse diffusion process, while the energy enables decoy ranking without relying on a separately trained confidence model. On the Docking Benchmark 5, DFMDock achieves a 4.9% Top‐1 and 31.6% Oracle success rate, outperforming DiffDock‐PP (4.3% and 16.2%, respectively). Unlike co‐folding models, DFMDock does not require multiple sequence alignments (MSAs) and generalizes to unseen targets. In decoy ranking, its learned energy function outperforms Rosetta energy and model‐derived confidence scores, producing funnel‐shaped energy landscapes enriched for near‐native structures. These results suggest DFMDock as an efficient and physically grounded approach to diffusion‐based protein docking.