Summary
MAELLE (Mechanistic Edit Flow-matching on Electron Rearrangements) is a machine learning model for chemical reaction prediction introduced by Nguyen Xuan-Vu, Octavian Susanu, and Daniel Armstrong (arXiv:2508.11366). Instead of de novo product generation or heuristic graph edits on molecular topology, MAELLE models reactions as discrete flow matching over electron occupation vectors. The reactant-to-product mapping is formulated as a Continuous-time Markov Chain (CTMC) over a graph-structured, integer-valued electron occupation space covering all bonding, non-bonding, and hydrogen sites. Intermediate edit trajectories are built by generalizing the discrete flow matching mixture path to discrete electron rearrangements via Optimal Transport, yielding mechanistically interpretable edit moves without requiring annotated elementary steps. MAELLE matches leading reaction prediction models on the USPTO-480K benchmark and, evaluated on two out-of-distribution settings (structural complexity and reaction type), retains strong performance where existing methods degrade. Because its learned flow operates over full electron redistribution, MAELLE naturally recovers mechanism trajectories consistent with known chemistry and can predict reaction by-products.
This forum post introduces the paper MAELLE: Mechanistic Edit Flow-matching on Electron Rearrangements (arXiv:2508.11366) by Nguyen Xuan-Vu, Octavian Susanu, and Daniel Armstrong, posted 2026-08-28 (ML).
Key Points
- Motivation: Chemical reactions are fundamentally transformations in electron space, but most ML approaches model them either via de novo generation of product molecules or via heuristic graph edits on molecular topology.
- Approach: MAELLE models reactions as discrete flow matching over electron occupation vectors. The reactant-to-product mapping is formulated as a Continuous-time Markov Chain (CTMC) over a graph-structured, integer-valued electron occupation space defined on all bonding, non-bonding, and hydrogen sites.
- Trajectory construction: The discrete flow matching mixture path is generalized to discrete electron rearrangements using Optimal Transport, producing a series of mechanistically interpretable edit moves without requiring annotated elementary steps.
Results
- Achieves performance comparable to leading reaction prediction models on the USPTO-480K benchmark.
- Evaluated on two out-of-distribution settings — structural complexity and reaction type — where MAELLE maintains strong performance while existing methods degrade.
- Because the learned flow operates over full electron redistribution, MAELLE naturally recovers mechanistic trajectories consistent with known chemistry and can predict reaction by-products.
Paper link: https://arxiv.org/abs/2508.11366
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