mlsanityData to 7 Oct 2026

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

arXiv 1709.04555 ↗First posted 13 Sep 2017cs.LG cs.AI stat.MLcited by 102

Not on the map. Why?

Abstract (arXiv)

The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers from coverage and efficiency issues. In this paper, we propose a template-free approach to efficiently explore the space of product molecules by first pinpointing the reaction center -- the set of nodes and edges where graph edits occur. Since only a small number of atoms contribute to reaction center, we can directly enumerate candidate products. The generated candidates are scored by a Weisfeiler-Lehman Difference Network that models high-order interactions between changes occurring at nodes across the molecule. Our framework outperforms the top-performing template-based approach with a 10% margin, while running orders of magnitude faster. Finally, we demonstrate that the model accuracy rivals the performance of domain experts.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 18; 2023: 21; 2024: 17; 2025: 21; 2026: 14.

102 papers.

Sort:

By area:

  1. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  2. 2022Graph neural networks for materials science and chemistrycited by 181
  3. 2023What can Large Language Models do in chemistry? A comprehensive benchmark on eight taskscited by 179
  4. 2021Geometric Deep Learning on Molecular Representationscited by 113
  5. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  6. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 96
  7. 2022SELFIES and the future of molecular string representationscited by 76
  8. 2021Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction predictioncited by 66
  9. 2022Ordered Subgraph Aggregation Networkscited by 57
  10. 2021A Survey of Machine Learning for Computer Architecture and Systemscited by 53
  11. 2022Graph-based Molecular Representation Learningcited by 51
  12. 2024Developing ChemDFM as a large language foundation model for chemistrycited by 51
38 more
  1. 2022Root-aligned SMILES: A Tight Representation for Chemical Reaction Predictioncited by 49
  2. 2022TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoverycited by 45
  3. 2023SELFormer: Molecular Representation Learning via SELFIES Language Modelscited by 40
  4. 2021Chemical-Reaction-Aware Molecule Representation Learningcited by 36
  5. 2023Probabilistically Rewired Message-Passing Neural Networkscited by 26
  6. 2022Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Modelcited by 25
  7. 2021Self-Improved Retrosynthetic Planningcited by 23
  8. 2025ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledgecited by 20
  9. 2024SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Modelscited by 19
  10. 2025Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skillscited by 19
  11. 2022BARTSmiles: Generative Masked Language Models for Molecular Representationscited by 18
  12. 2021Non-Autoregressive Electron Redistribution Modeling for Reaction Predictioncited by 17
  13. 2023Conformal Autoregressive Generation: Beam Search with Coverage Guaranteescited by 16
  14. 2023Retrosynthesis Prediction with Local Template Retrievalcited by 15
  15. 2024Probabilistic Graph Rewiring via Virtual Nodescited by 15
  16. 2021RetroComposer: Composing Templates for Template-Based Retrosynthesis Predictioncited by 13
  17. 2023ReactionT5: a large-scale pre-trained model towards application of limited reaction datacited by 13
  18. 2025Electron flow matching for generative reaction mechanism prediction obeying conservation lawscited by 13
  19. 2022Few-Shot Learning on Graphscited by 11
  20. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  21. 2024RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNetscited by 10
  22. 2024Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Modelscited by 7
  23. 2021Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Designcited by 6
  24. 2022Revisiting the role of heterophily in graph representation learning: An edge classification perspectivecited by 6
  25. 2023Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representationscited by 6
  26. 2023Doubly Stochastic Graph-based Non-autoregressive Reaction Predictioncited by 6
  27. 2024UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignmentcited by 6
  28. 2025Challenging reaction prediction models to generalize to novel chemistrycited by 6
  29. 2023Recent advances in artificial intelligence for retrosynthesiscited by 5
  30. 2023AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learningcited by 5
  31. 2024Retrosynthesis prediction enhanced by in-silico reaction data augmentationcited by 5
  32. 2024Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Predictioncited by 5
  33. 2023Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Modelcited by 4
  34. 2023GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discoverycited by 4
  35. 2023A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challengescited by 4
  36. 2023Holistic chemical evaluation reveals pitfalls in reaction prediction modelscited by 4
  37. 2025Procrustean Bed for AI-Driven Retrosynthesis: A Unified Framework for Reproducible Evaluationcited by 4
  38. 2022Knowledge-informed Molecular Learning: A Survey on Paradigm Transfercited by 3

50 most cited of 102 All 102 (JSON)