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

Chemical reactions span diverse molecules and transformation types, but available data cover them unevenly, making prediction beyond the training distribution a central challenge. Chemists reason about reactivity through arrow-pushing: electrons move from a donor site to an acceptor site, and that direction helps determine which bonds form. Most reaction predictors instead generate products or edit molecular topology without explicitly representing these transfers. We introduce MAELLE, a discrete flow-matching model that predicts directional electron-pair edits on a graph of bonding, non-bonding, and hydrogen sites. Optimal transport infers an unordered set of edits from each reactant–product pair, allowing training without annotated elementary steps. The resulting stochastic trajectories retain the reactants' heavy atoms. MAELLE achieves 93.9% top-5 accuracy on USPTO-480K and 59.3% top-5 accuracy on the literature-derived AbSynth benchmark, the highest AbSynth result among the evaluated baselines. In a separate analysis of expert-annotated reactions, its electron sources and sinks agree with the annotations for 95.4% of recovered persistent bond-forming steps in trajectories reaching the recorded product. These results support endpoint-supervised directional electron flow as a useful representation for reaction prediction beyond pattern recognition.