mlsanityData to 7 Oct 2026

Learning Graph Models for Retrosynthesis Prediction

arXiv 2006.07038 ↗First posted 12 Jun 2020cs.LG stat.MLcited by 64

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Abstract (arXiv)

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of 53.7%, outperforming previous template-free and semi-template-based methods.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2020.

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Citing papers per year: 2021: 2; 2022: 9; 2023: 17; 2024: 14; 2025: 12; 2026: 10.

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  1. 2022Graph neural networks for materials science and chemistrycited by 181
  2. 2024LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Datasetcited by 124
  3. 2021Geometric Deep Learning on Molecular Representationscited by 113
  4. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 96
  5. 2023Everything is Connected: Graph Neural Networkscited by 85
  6. 2022Root-aligned SMILES: A Tight Representation for Chemical Reaction Predictioncited by 49
  7. 2023RetroBridge: Modeling Retrosynthesis with Markov Bridgescited by 35
  8. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  9. 2023ReLM: Leveraging Language Models for Enhanced Chemical Reaction Predictioncited by 24
  10. 2022Graph Neural Networks for Moleculescited by 21
  11. 2023Retrosynthesis Prediction with Local Template Retrievalcited by 15
  12. 2023Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planningcited by 15
38 more
  1. 2022FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planningcited by 14
  2. 2024PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomescited by 14
  3. 2021RetroComposer: Composing Templates for Template-Based Retrosynthesis Predictioncited by 13
  4. 2022SemiRetro: Semi-template framework boosts deep retrosynthesis predictioncited by 13
  5. 2025LLM-Augmented Chemical Synthesis and Design Decision Programscited by 13
  6. 2025AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Searchcited by 13
  7. 2024DirectMultiStep: Direct Route Generation for Multistep Retrosynthesiscited by 11
  8. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  9. 2024RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNetscited by 10
  10. 2023Predictive Chemistry Augmented with Text Retrievalcited by 9
  11. 2024BatGPT-Chem: A Foundation Large Model For Retrosynthesis Predictioncited by 9
  12. 2022MARS: A Motif-based Autoregressive Model for Retrosynthesis Predictioncited by 8
  13. 2024Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledgecited by 8
  14. 2024Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Modelscited by 7
  15. 2024UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignmentcited by 6
  16. 2024Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Modelscited by 6
  17. 2022G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Trainingcited by 5
  18. 2023Recent advances in artificial intelligence for retrosynthesiscited by 5
  19. 2023RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learningcited by 5
  20. 2024Retrosynthesis prediction enhanced by in-silico reaction data augmentationcited by 5
  21. 2024Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Predictioncited by 5
  22. 2022mathsfG2Retro as a Two-Step Graph Generative Models for Retrosynthesis Predictioncited by 4
  23. 2023A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challengescited by 4
  24. 2025Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstrationcited by 4
  25. 2026Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesiscited by 4
  26. 2023RLSynC: Offline-Online Reinforcement Learning for Synthon Completioncited by 3
  27. 2025Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunitiescited by 3
  28. 2025RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learningcited by 3
  29. 2025Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesiscited by 3
  30. 2025Trustworthy Retrosynthesis: Eliminating Hallucinations with a Diverse Ensemble of Reaction Scorerscited by 3
  31. 2026Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matchingcited by 3
  32. 2023MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editingcited by 2
  33. 2023ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patternscited by 2
  34. 2023Evolutionary Retrosynthetic Route Planningcited by 2
  35. 2023RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Predictioncited by 2
  36. 2025Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPscited by 2
  37. 2025PolyGraph Discrepancy: a classifier-based metric for graph generationcited by 2
  38. 2025BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generationcited by 2

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