  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. In this work, we propose a similar approach for rigid
protein-protein docking: DiffDock-PP is a diffusion generative model that
learns to translate and rotate unbound protein structures into their bound
conformations. We achieve state-of-the-art performance on DIPS with a median
C-RMSD of 4.85, outperforming all considered baselines. Additionally,
DiffDock-PP is faster than all search-based methods and generates reliable
confidence estimates for its predictions. Our code is publicly available at
$\texttt{https://github.com/ketatam/DiffDock-PP}$
