mlsanityData to 7 Oct 2026

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

arXiv 2206.08515 ↗First posted 17 Jun 2022cs.LGcited by 69

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Abstract (arXiv)

Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the underlinecomplete and underlineefficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (urlhttps://github.com/divelab/DIG).

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). Builds on 48 of its 68 references that are in the map. First posted on arXiv 17 Jun 2022.

Builds on

48 of 68 references.

References · 48

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  6. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  7. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  8. 2021E(n) Equivariant Graph Neural Networkscited by 951
  9. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  10. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  11. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  12. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
36 more
  1. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  2. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 666
  3. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  4. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  5. 2020Directional Message Passing for Molecular Graphscited by 556
  6. 2019Graph U-Netscited by 489
  7. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  8. 2019Provably Powerful Graph Networkscited by 385
  9. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  10. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  11. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  12. 2019Cormorant: Covariant Molecular Neural Networkscited by 262
  13. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  14. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  15. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  16. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  17. 2021Spherical Message Passing for 3D Graph Networkscited by 166
  18. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  19. 2021Geometric Deep Learning on Molecular Representationscited by 113
  20. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  21. 2020Building powerful and equivariant graph neural networks with structural message-passingcited by 95
  22. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  23. 2019Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspectivecited by 73
  24. 2021ForceNet: A Graph Neural Network for Large-Scale Quantum Calculationscited by 58
  25. 2020Symmetry-Aware Actor-Critic for 3D Molecular Designcited by 55
  26. 2021Rotation Invariant Graph Neural Networks using Spin Convolutionscited by 52
  27. 2020Reinforcement Learning for Molecular Design Guided by Quantum Mechanicscited by 51
  28. 2020Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discoverycited by 46
  29. 2020Topology-Aware Graph Pooling Networkscited by 44
  30. 2021Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphscited by 31
  31. 2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
  32. 2020Molecular machine learning with conformer ensemblescited by 27
  33. 2020Deep Learning of High-Order Interactions for Protein Interface Predictioncited by 22
  34. 2021Geometric Transformers for Protein Interface Contact Predictioncited by 18
  35. 2021Learning ground states of quantum Hamiltonians with graph networkscited by 12
  36. 2022Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systemscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 19; 2024: 17; 2025: 19; 2026: 7.

69 papers.

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  1. 2023On the Expressive Power of Geometric Graph Neural Networkscited by 89
  2. 2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87
  3. 2022One Transformer Can Understand Both 2D & 3D Molecular Datacited by 77
  4. 2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
  5. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 65
  6. 2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 54
  7. 2022Graph-based Molecular Representation Learningcited by 51
  8. 2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 46
  9. 2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
  10. 2023Efficient Approximations of Complete Interatomic Potentials for Crystal Property Predictioncited by 37
  11. 2024Complete and Efficient Graph Transformers for Crystal Material Property Predictioncited by 34
  12. 2023A Latent Diffusion Model for Protein Structure Generationcited by 28
38 more
  1. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  2. 2023QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Moleculescited by 26
  3. 2023MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Designcited by 23
  4. 2024Equivariance via Minimal Frame Averaging for More Symmetries and Efficiencycited by 18
  5. 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?cited by 18
  6. 2022Geometry-Complete Perceptron Networks for 3D Molecular Graphscited by 15
  7. 2022Graph Neural Network with Local Frame for Molecular Potential Energy Surfacecited by 14
  8. 2023De novo protein design using geometric vector field networkscited by 14
  9. 2025Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systemscited by 13
  10. 2025E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Productscited by 12
  11. 2025Invariant Tokenization of Crystalline Materials for Language Model Enabled Generationcited by 12
  12. 2024On the Completeness of Invariant Geometric Deep Learning Modelscited by 11
  13. 2023Complete Neural Networks for Complete Euclidean Graphscited by 10
  14. 2024Geometry Informed Tokenization of Molecules for Language Model Generationcited by 10
  15. 2024AI-driven inverse design of materials: Past, present and futurecited by 10
  16. 2024On the Expressive Power of Sparse Geometric MPNNscited by 7
  17. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  18. 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
  19. 2024Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Modelscited by 6
  20. 2024GeoMFormer: A General Architecture for Geometric Molecular Representation Learningcited by 6
  21. 2026MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentialscited by 6
  22. 2023GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoisingcited by 5
  23. 2023AtomSurf : Surface Representation for Learning on Protein Structurescited by 5
  24. 2025Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunitiescited by 5
  25. 2025Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Framescited by 5
  26. 2023GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shiftscited by 4
  27. 2024Molecule Graph Networks with Many-body Equivariant Interactionscited by 3
  28. 2025RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanationcited by 3
  29. 2025Universally Invariant Learning in Equivariant GNNscited by 3
  30. 2026Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fieldscited by 3
  31. 2022End-to-end AI framework for interpretable prediction of molecular and crystal propertiescited by 2
  32. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  33. 2024SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learningcited by 2
  34. 2025Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modelingcited by 2
  35. 2023PerCNet: Periodic Complete Representation for Crystal Graphscited by 1
  36. 2024Introducing Diminutive Causal Structure into Graph Representation Learningcited by 1
  37. 2024FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fieldscited by 1
  38. 2025Bidirectional Hierarchical Protein Multi-Modal Representation Learningcited by 1

50 most cited of 69 All 69 (JSON)