mlsanityData to 7 Oct 2026

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

arXiv 2202.05146 ↗First posted 7 Feb 2022q-bio.BM cs.LGcited by 143

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Abstract (arXiv)

Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.

Key facts. Cited by 143 papers in the map (data through 7 Oct 2026). Builds on 21 of its 69 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

21 of 69 references.

References · 21

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Group Equivariant Convolutional Networkscited by 1,154
  3. 2021E(n) Equivariant Graph Neural Networkscited by 951
  4. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  5. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  6. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  7. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  8. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
  9. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
  10. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  11. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  12. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
9 more
  1. 2021Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Dockingcited by 81
  2. 2021Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinitycited by 60
  3. 2016Protein-Ligand Scoring with Convolutional Neural Networkscited by 57
  4. 2018DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networkscited by 52
  5. 2015AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discoverycited by 38
  6. 2020Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Modelscited by 33
  7. 2018End-to-End Learning on 3D Protein Structure for Interface Predictioncited by 32
  8. 2019Combining docking pose rank and structure with deep learning improves protein-ligand binding mode predictioncited by 10
  9. 2021Structure-aware generation of drug-like moleculescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 26; 2023: 42; 2024: 35; 2025: 29; 2026: 11.

143 papers.

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  1. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  2. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  3. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  4. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 176
  5. 2023Everything is Connected: Graph Neural Networkscited by 85
  6. 2022PiFold: Toward effective and efficient protein inverse foldingcited by 82
  7. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  8. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  9. 2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 54
  10. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
  11. 2022Structure-based drug design with geometric deep learningcited by 40
  12. 2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
38 more
  1. 2023Geometry-Complete Diffusion for 3D Molecule Generation and Optimizationcited by 36
  2. 2024On the Scalability of GNNs for Molecular Graphscited by 36
  3. 2022State-specific protein-ligand complex structure prediction with a multi-scale deep generative modelcited by 35
  4. 2022E3Bind: An End-to-End Equivariant Network for Protein-Ligand Dockingcited by 35
  5. 2023Fractional Denoising for 3D Molecular Pre-trainingcited by 33
  6. 2023IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Researchcited by 29
  7. 2023Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Designcited by 29
  8. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  9. 2022Interpretable Geometric Deep Learning via Learnable Randomness Injectioncited by 27
  10. 2024Deep Confident Steps to New Pockets: Strategies for Docking Generalizationcited by 27
  11. 2023Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learningcited by 26
  12. 2024Tx-LLM: A Large Language Model for Therapeuticscited by 23
  13. 2022Evaluating Self-Supervised Learning for Molecular Graph Embeddingscited by 22
  14. 2022Graph Neural Networks for Moleculescited by 21
  15. 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latentscited by 20
  16. 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equationcited by 18
  17. 2023Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networkscited by 18
  18. 2024Equivariance via Minimal Frame Averaging for More Symmetries and Efficiencycited by 18
  19. 2023FABind: Fast and Accurate Protein-Ligand Bindingcited by 17
  20. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  21. 2023AbODE: Ab Initio Antibody Design using Conjoined ODEscited by 15
  22. 2022Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networkscited by 14
  23. 2022Machine learning and invariant theorycited by 13
  24. 2024How Interpretable Are Interpretable Graph Neural Networks?cited by 13
  25. 2024ReactZyme: A Benchmark for Enzyme-Reaction Predictioncited by 13
  26. 2023Protein-ligand binding representation learning from fine-grained interactionscited by 12
  27. 2024Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physicscited by 12
  28. 2023Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?cited by 11
  29. 2023Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinementcited by 11
  30. 2022Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactionscited by 10
  31. 2023Towards fully covariant machine learningcited by 10
  32. 2024FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generationcited by 8
  33. 2024FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Predictioncited by 8
  34. 2023Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Predictioncited by 7
  35. 2023CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generationcited by 7
  36. 2025PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Dockingcited by 7
  37. 2025Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generationcited by 7
  38. 2024Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chainscited by 6

50 most cited of 143 All 143 (JSON)