mlsanityData to 7 Oct 2026

Neural Message Passing for Quantum Chemistry

arXiv 1704.01212 ↗First posted 4 Apr 2017cs.LGcited by 3,460

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Cited by 42 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.

Key facts. Cited by 3,460 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2017.

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Citing papers per year: 2020: 1; 2021: 559; 2022: 692; 2023: 673; 2024: 645; 2025: 502; 2026: 388.

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  2. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  3. 2021E(n) Equivariant Graph Neural Networkscited by 951
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  6. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
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  8. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  9. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  10. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  11. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  12. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
38 more
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  4. 2022Message Passing Neural PDE Solverscited by 368
  5. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  6. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  7. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
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  9. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  10. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  11. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  12. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  13. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  14. 2022Transformer for Partial Differential Equations' Operator Learningcited by 270
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  16. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
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  19. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
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  21. 2021A Survey on Deep Semi-supervised Learningcited by 250
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  26. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
  27. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  28. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  29. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  30. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
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  33. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
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  35. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  36. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
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  38. 2022Pure Transformers are Powerful Graph Learnerscited by 188

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