mlsanityData to 7 Oct 2026

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

arXiv 2205.07249 ↗First posted 15 May 2022cs.LG q-bio.BMcited by 182

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Cited by 55 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the specificity and success rate of in silico drug design by considering the structure of protein pockets. This setting posts fundamental computational challenges in sampling new chemical compounds that could satisfy multiple geometrical constraints imposed by pockets. Previous sampling algorithms either sample in the graph space or only consider the 3D coordinates of atoms while ignoring other detailed chemical structures such as bond types and functional groups. To address the challenge, we develop Pocket2Mol, an E(3)-equivariant generative network composed of two modules: 1) a new graph neural network capturing both spatial and bonding relationships between atoms of the binding pockets and 2) a new efficient algorithm which samples new drug candidates conditioned on the pocket representations from a tractable distribution without relying on MCMC. Experimental results demonstrate that molecules sampled from Pocket2Mol achieve significantly better binding affinity and other drug properties such as druglikeness and synthetic accessibility.

Key facts. Cited by 182 papers in the map (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 15 May 2022.

Builds on

27 of 43 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Group Equivariant Convolutional Networkscited by 1,154
  4. 2021E(n) Equivariant Graph Neural Networkscited by 951
  5. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  6. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  7. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  8. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
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  10. 2020Directional Message Passing for Molecular Graphscited by 556
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15 more
  1. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 348
  2. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  3. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  4. 2017Toward Multimodal Image-to-Image Translationcited by 295
  5. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 270
  6. 2019Cormorant: Covariant Molecular Neural Networkscited by 262
  7. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  8. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 171
  9. 2022A 3D Generative Model for Structure-Based Drug Designcited by 148
  10. 2018Molecular Hypergraph Grammar with its Application to Molecular Optimizationcited by 56
  11. 2020Symmetry-Aware Actor-Critic for 3D Molecular Designcited by 55
  12. 2021Equivariant Graph Neural Networks for 3D Macromolecular Structurecited by 53
  13. 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
  14. 2020Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Modelscited by 33
  15. 2018Generating equilibrium molecules with deep neural networkscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 29; 2024: 51; 2025: 55; 2026: 39.

182 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  3. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  4. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 176
  5. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  6. 2024DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Designcited by 87
  7. 2023MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generationcited by 65
  8. 2023Equivariant Flow Matching with Hybrid Probability Transportcited by 59
  9. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  10. 2024Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generationcited by 53
  11. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
  12. 2024MolCRAFT: Structure-Based Drug Design in Continuous Parameter Spacecited by 48
38 more
  1. 2024SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matchingcited by 40
  2. 20233D molecule generation by denoising voxel gridscited by 38
  3. 2023Geometry-Complete Diffusion for 3D Molecule Generation and Optimizationcited by 36
  4. 2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
  5. 2023Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaborationcited by 30
  6. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  7. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  8. 2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
  9. 2024DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimizationcited by 27
  10. 2024PPFlow: Target-aware Peptide Design with Torsional Flow Matchingcited by 26
  11. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  12. 2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25
  13. 2024Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimizationcited by 25
  14. 2023Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3Dcited by 24
  15. 2025LIDDIA: Language-based Intelligent Drug Discovery Agentcited by 24
  16. 2022Evaluating Self-Supervised Learning for Molecular Graph Embeddingscited by 22
  17. 2024Generative Flows on Synthetic Pathway for Drug Designcited by 20
  18. 2025PharmAgents: Building a Virtual Pharma with Large Language Model Agentscited by 20
  19. 2025FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generationcited by 20
  20. 2022Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Designcited by 19
  21. 2024CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graphcited by 19
  22. 2023Generalist Equivariant Transformer Towards 3D Molecular Interaction Learningcited by 18
  23. 2024Unified Generative Modeling of 3D Molecules via Bayesian Flow Networkscited by 18
  24. 2024Structure-based drug design by denoising voxel gridscited by 18
  25. 2025FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generationcited by 18
  26. 2025Multi-domain Distribution Learning for De Novo Drug Designcited by 15
  27. 2025Flow Matching Meets Biology and Life Science: A Surveycited by 14
  28. 2023Generation of 3D Molecules in Pockets via Language Modelcited by 13
  29. 2024Binding-Adaptive Diffusion Models for Structure-Based Drug Designcited by 12
  30. 2024TAGMol: Target-Aware Gradient-guided Molecule Generationcited by 12
  31. 2024BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learningcited by 12
  32. 2024Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Designcited by 11
  33. 2024Exploring Discrete Flow Matching for 3D De Novo Molecule Generationcited by 11
  34. 2025UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Designcited by 11
  35. 2022A Survey on Protein Representation Learning: Retrospect and Prospectcited by 10
  36. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  37. 2023An Equivariant Generative Framework for Molecular Graph-Structure Co-Designcited by 10
  38. 2024A Graph is Worth K Words: Euclideanizing Graph using Pure Transformercited by 10

50 most cited of 182 All 182 (JSON)