mlsanityData to 7 Oct 2026

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

arXiv 2304.03889 ↗First posted 8 Apr 2023q-bio.BM cs.LGcited by 23

On the map: Diffusion models → Flow matching, time series and science

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

Key facts. Cited by 23 papers in the map (data through 7 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.

Builds on

12 of 36 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Modeling Molecular Structures with Intrinsic Diffusion Models23 Feb
  2. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem8 Jun
  3. 2022Equivariant Diffusion for Molecule Generation in 3D31 Mar

Other references · 9

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  3. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  4. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  5. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  6. 2022Riemannian Score-Based Generative Modellingcited by 216
  7. 2021Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Dockingcited by 81
  8. 2018End-to-End Learning on 3D Protein Structure for Interface Predictioncited by 32
  9. 2018Functional Maps Representation on Product Manifoldscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 14; 2025: 3; 2026: 2.

23 papers.

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  1. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  2. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodiescited by 35
  3. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  4. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  5. 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latentscited by 20
  6. 2023Learning to design protein-protein interactions with enhanced generalizationcited by 15
  7. 2024Constraint-Aware Diffusion Models for Trajectory Optimizationcited by 11
  8. 2025Diffusion Generative Modeling on Lie Group Representationscited by 6
  9. 2025Aligning Diffusion Model with Problem Constraints for Trajectory Optimizationcited by 6
  10. 2024Revealing data leakage in protein interaction benchmarkscited by 5
  11. 2024Multi-level Interaction Modeling for Protein Mutational Effect Predictioncited by 5
  12. 2024Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactionscited by 5
11 more
  1. 2024Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Predictioncited by 4
  2. 2024Stable Autonomous Flow Matchingcited by 2
  3. 2024FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Framescited by 2
  4. 2025From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineeringcited by 2
  5. 2023DockGame: Cooperative Games for Multimeric Rigid Protein Dockingcited by 1
  6. 2024Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomoleculescited by 1
  7. 2024BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Designcited by 1
  8. 2024PROflow: An iterative refinement model for PROTAC-induced structure predictioncited by 0
  9. 2024Hessian-Informed Flow Matchingcited by 0
  10. 2026EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learningcited by 0
  11. 2026TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complexcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
  2. 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
  3. 2025SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion
  4. 2024FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
  5. 2024Deep Confident Steps to New Pockets: Strategies for Docking Generalization
  6. 2025Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
  7. 2024Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
  8. 2025A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization