Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

2026-08-27

AI Summary

本文通过在图结构电子占据上使用离散流匹配方法，解决了化学反应预测问题，并在多个基准测试中表现出色。

Abstract

Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through \textit{de novo} generation of product molecules or through heuristic graph edits that operate directly on molecular topology. We introduce MAELLE (\textbf{M}ech\textbf{A}nistic \textbf{E}dit f\textbf{L}ow-matching on e\textbf{L}ectron r\textbf{E}arrangements), which instead models reactions as discrete flow matching over electron occupation vectors. Concretely, we formulate the reactant-to-product mapping as a Continuous-time Markov Chain (CTMC) over the graph-structured integer-valued electron occupation space defined on all bonding, non-bonding, and hydrogen sites. To construct the intermediate edit trajectories, we generalize the discrete flow matching mixture path to discrete electron rearrangements using Optimal Transport, yielding a sequence of mechanistically interpretable edit moves without requiring elementary step annotations. MAELLE achieves competitive performance on the USPTO-480K benchmark compared with leading reaction prediction models. Beyond in-distribution accuracy, we evaluate robustness across two out-of-distribution settings - structural complexity and reaction type - and find that MAELLE maintains strong performance where existing methods degrade. Finally, because the learned flow operates over the full electron redistribution, MAELLE naturally recovers mechanistic trajectories that align with known chemistry and can predict side products of a reaction.

Problem

• Chemical Reactions
• Electron Occupation
• Discrete Flow Matching
• Mechanistic Prediction
• Reaction Trajectories

Innovation

• Discrete Flow Matching
• Electron Occupation Vectors
• Mechanistic Interpretability
• Optimal Transport
• Continuous-time Markov Chain (CTMC)