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RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis

arXiv 2609.38484 ↗First posted 29 Sep 2026cs.LG q-bio.QMcited by 0

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Abstract (arXiv)

Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials design. Retrosynthesis involves molecular graph transformations that can change both connectivity and graph size. These transformations may introduce reactant components absent from the target while revising the product-derived structure. To model these transformations, we propose RetroGEF, a flow-based generative model for single-step retrosynthesis. Starting from the target molecule, it constructs possible reactants by adding atoms and changing bonds in the molecular graph. RetroGEF models molecular transformations and changes in graph size within the same generative process, rather than relying on a fixed-size graph canvas. It learns this process directly from product--reactant pairs without requiring a prescribed edit order. Experiments on representative retrosynthesis benchmarks demonstrate that RetroGEF achieves state-of-the-art performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 49 references that are in the map. First posted on arXiv 29 Sep 2026.

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References · 36

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24 more
  1. 2017Learning to Plan Chemical Synthesescited by 175
  2. 2020Retrosynthesis Prediction with Conditional Graph Logic Networkcited by 103
  3. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 96
  4. 2020Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Editscited by 87
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  14. 2022Root-aligned SMILES: A Tight Representation for Chemical Reaction Predictioncited by 49
  15. 2023RetroBridge: Modeling Retrosynthesis with Markov Bridgescited by 35
  16. 2023Trans-Dimensional Generative Modeling via Jump Diffusion Modelscited by 33
  17. 2025AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Searchcited by 13
  18. 2023Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesiscited by 9
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  20. 2024Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Modelscited by 7
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  23. 2025RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesiscited by 4
  24. 2026Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matchingcited by 3

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