DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models

Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulated protein-small molecule docking as a generative problem with significant performance boosts over both traditional and deep learning baselines. We achieve state-ofthe-art performance on DIPS with a median C-RMSD of 4.85, outperforming all considered baselines. Proteins realize their myriad biological functions through interactions with biomolecules, such as other proteins, nucleic acids, or small molecules. The presence or absence of such interactions is dictated in part by the geometric and chemical complementarity of participating bodies. Thus, learning how individual proteins form complexes is crucial to understanding protein activity.