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

arXiv: https://arxiv.org/abs/2304.03889 · HTML: https://mlsanity.com/paper/2304.03889/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 12 of 12)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds](https://mlsanity.com/paper/1802.08219/) (2018)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [Torsional Diffusion for Molecular Conformer Generation](https://mlsanity.com/paper/2206.01729/) (2022)
- [Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem](https://mlsanity.com/paper/2206.04119/) (2022)
- [Riemannian Score-Based Generative Modelling](https://mlsanity.com/paper/2202.02763/) (2022)
- [Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking](https://mlsanity.com/paper/2111.07786/) (2021)
- [End-to-End Learning on 3D Protein Structure for Interface Prediction](https://mlsanity.com/paper/1807.01297/) (2018)
- [Functional Maps Representation on Product Manifolds](https://mlsanity.com/paper/1809.10940/) (2018)
- [Modeling Molecular Structures with Intrinsic Diffusion Models](https://mlsanity.com/paper/2302.12255/) (2023)

## Built on by (most cited first, 24 of 24)

- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies](https://mlsanity.com/paper/2308.05027/) (2023)
- [A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications](https://mlsanity.com/paper/2403.00485/) (2024)
- [Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials](https://mlsanity.com/paper/2409.04481/) (2024)
- [DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents](https://mlsanity.com/paper/2407.03300/) (2024)
- [Learning to design protein-protein interactions with enhanced generalization](https://mlsanity.com/paper/2310.18515/) (2023)
- [Constraint-Aware Diffusion Models for Trajectory Optimization](https://mlsanity.com/paper/2406.00990/) (2024)
- [Diffusion Generative Modeling on Lie Group Representations](https://mlsanity.com/paper/2502.02513/) (2025)
- [Aligning Diffusion Model with Problem Constraints for Trajectory Optimization](https://mlsanity.com/paper/2504.00342/) (2025)
- [Revealing data leakage in protein interaction benchmarks](https://mlsanity.com/paper/2404.10457/) (2024)
- [Multi-level Interaction Modeling for Protein Mutational Effect Prediction](https://mlsanity.com/paper/2405.17802/) (2024)
- [Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions](https://mlsanity.com/paper/2410.09543/) (2024)
- [Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction](https://mlsanity.com/paper/2401.08986/) (2024)
- [Stable Autonomous Flow Matching](https://mlsanity.com/paper/2402.05774/) (2024)
- [FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames](https://mlsanity.com/paper/2407.01649/) (2024)
- [From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering](https://mlsanity.com/paper/2501.02680/) (2025)
- [DockGame: Cooperative Games for Multimeric Rigid Protein Docking](https://mlsanity.com/paper/2310.06177/) (2023)
- [Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules](https://mlsanity.com/paper/2406.01622/) (2024)
- [BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design](https://mlsanity.com/paper/2409.16298/) (2024)
- [EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning](https://mlsanity.com/paper/2606.04154/) (2026)
- [PROflow: An iterative refinement model for PROTAC-induced structure prediction](https://mlsanity.com/paper/2405.06654/) (2024)
- [Hessian-Informed Flow Matching](https://mlsanity.com/paper/2410.11433/) (2024)
- [TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex](https://mlsanity.com/paper/2607.22143/) (2026)
- [Improving scoring functions for protein-protein docking with LambdaLoss](https://mlsanity.com/paper/2610.00191/) (2026)

## Abstract (arXiv)

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 texttthttps://github.com/ketatam/DiffDock-PP