Summary
MAELLE (Mechanistic Edit Flow-matching on Electron rearrangements) is a machine learning approach to chemical reaction prediction that models reactions as discrete flow matching over electron occupation vectors rather than de novo product generation or heuristic graph edits. Developed by Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong, and Philippe Schwaller (arXiv:2608.27429), the method formulates the reactant-to-product mapping 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 constructed by generalizing discrete flow matching mixing paths to discrete electron rearrangements using optimal transport, yielding mechanistically interpretable edit moves without requiring elementary-step annotations. MAELLE achieves competitive performance with leading reaction prediction models on the USPTO-480K benchmark, and notably maintains strong performance in two out-of-distribution settings—structural complexity and reaction type—where existing methods degrade. This makes it a robust, interpretable alternative for reaction prediction.
Overview
Field: Machine Learning
Authors: Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong, Philippe Schwaller
Published: 2026-08-27
arXiv: 2608.27429
Summary
Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through *de novo* generation of product molecules or through heuristic graph edits that operate directly on molecular topology. The authors introduce MAELLE (MechAnistic Edit fLow-matching on eLectron rEarrangements), which instead models reactions as discrete flow matching over electron occupation vectors.
Key ideas
- 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.
- To construct intermediate edit trajectories, the authors generalize the discrete flow matching mixing paths to discrete electron rearrangements using optimal transport.
- This produces a series of mechanistically interpretable edit moves without requiring elementary-step annotations.
Results
- MAELLE achieves competitive performance with leading reaction prediction models on the USPTO-480K benchmark.
- In two out-of-distribution settings — structural complexity and reaction type — MAELLE maintains strong performance while existing methods degrade.
---
*Auto-collected on 2026-08-30.*
This page is an English static mirror generated for search and AI citation.
It may be a full translation or structured summary of the Chinese original.
Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/178634233