mlsanityData to 7 Oct 2026

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

arXiv 2111.07786 ↗First posted 15 Nov 2021cs.AI cs.LGcited by 81

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Abstract (arXiv)

Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e.g. drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no conformational change within the proteins happens during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position relative to the second protein. We mathematically guarantee a basic principle: the predicted complex is always identical regardless of the initial locations and orientations of the two structures. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment, achieved through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements and often outperform existing docking software despite not relying on heavy candidate sampling, structure refinement, or templates.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). Builds on 23 of its 82 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

23 of 82 references.

References · 23

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  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  8. 2021E(n) Equivariant Graph Neural Networkscited by 951
  9. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  10. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
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11 more
  1. 2020Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Datacited by 248
  2. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
  3. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  4. 2020LieTransformer: Equivariant self-attention for Lie Groupscited by 100
  5. 2021Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designcited by 82
  6. 2020Group Equivariant Stand-Alone Self-Attention For Visioncited by 63
  7. 2021Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinitycited by 60
  8. 2019Equivariant Hamiltonian Flowscited by 39
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 23; 2023: 24; 2024: 26; 2025: 3; 2026: 5.

81 papers.

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  1. 2022Riemannian Score-Based Generative Modellingcited by 216
  2. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  3. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 143
  4. 2024DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Designcited by 87
  5. 2022Conditional Antibody Design as 3D Equivariant Graph Translationcited by 75
  6. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  7. 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 55
  8. 2022Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learningcited by 54
  9. 2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 54
  10. 2023Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulationcited by 51
  11. 2024xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Proteincited by 49
  12. 2023End-to-End Full-Atom Antibody Designcited by 48
38 more
  1. 2023Graph Denoising Diffusion for Inverse Protein Foldingcited by 44
  2. 2022Structure-based drug design with geometric deep learningcited by 40
  3. 2023Aligned Diffusion Schr"odinger Bridgescited by 38
  4. 2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
  5. 2022E3Bind: An End-to-End Equivariant Network for Protein-Ligand Dockingcited by 35
  6. 2024Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3Dcited by 32
  7. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  8. 2023DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Modelscited by 23
  9. 2022Graph Neural Networks for Moleculescited by 21
  10. 2022Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transportcited by 19
  11. 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equationcited by 18
  12. 2023FABind: Fast and Accurate Protein-Ligand Bindingcited by 17
  13. 2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridgecited by 16
  14. 2022Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinementcited by 15
  15. 2023AbODE: Ab Initio Antibody Design using Conjoined ODEscited by 15
  16. 2023Learning to design protein-protein interactions with enhanced generalizationcited by 15
  17. 2022Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networkscited by 14
  18. 2024VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identificationcited by 14
  19. 2022CoSP: Co-supervised pretraining of pocket and ligandcited by 12
  20. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  21. 2022Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transportcited by 9
  22. 2024Equivariant Symmetry Breaking Setscited by 9
  23. 2024Learning the Language of Protein Structurecited by 9
  24. 2023Learning Harmonic Molecular Representations on Riemannian Manifoldcited by 8
  25. 2023Policy Gradient Methods in the Presence of Symmetries and State Abstractionscited by 8
  26. 2023Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Predictioncited by 7
  27. 2025Unified Guidance for Geometry-Conditioned Molecular Generationcited by 7
  28. 2024Revealing data leakage in protein interaction benchmarkscited by 5
  29. 2024Deep Reinforcement Learning for Modelling Protein Complexescited by 5
  30. 2024Multi-level Interaction Modeling for Protein Mutational Effect Predictioncited by 5
  31. 2024General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Designcited by 5
  32. 2024Mask prior-guided denoising diffusion improves inverse protein foldingcited by 5
  33. 2022Discovering the Representation Bottleneck of Graph Neural Networkscited by 4
  34. 2023PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modelingcited by 4
  35. 2023Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structurecited by 4
  36. 2024Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Predictioncited by 4
  37. 2024SE(3)-bi-equivariant Transformers for Point Cloud Assemblycited by 4
  38. 2025SE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradationcited by 4

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