AI 中文总结
提出MAELLE模型，将反应建模为电子占据向量的离散流匹配，在USPTO-480K基准上表现具竞争力，且分布外鲁棒性优于现有方法，可预测反应副产物。
AI 中文摘要
化学反应本质上是电子空间中的转变，但大多数机器学习方法要么通过从头生成产物分子来建模，要么通过直接作用于分子拓扑的启发式图编辑来建模。我们提出MAELLE（电子重排上的机理编辑流匹配，MechAnistic Edit fLow-matching on eLectron rEar-rangements），该方法将反应建模为电子占据向量上的离散流匹配。具体而言，我们将反应物到产物的映射公式化为连续时间马尔可夫链（CTMC），该链作用于所有成键、非成键和氢位点上定义的图结构整数电子占据空间。为构建中间编辑轨迹，我们使用最优传输将离散流匹配混合路径推广到离散电子重排，得到一系列具有机理解释的编辑操作，无需基元步骤注释。MAELLE在USPTO-480K基准上与领先的反应预测模型相比取得了有竞争力的性能。除了分布内准确率，我们在结构复杂性和反应类型两种分布外设置中评估鲁棒性，发现MAELLE在现有方法性能下降的情况下仍保持强劲表现。最后，由于学习到的流作用于完整的电子重分布，MAELLE可自然恢复与已知化学一致的机理轨迹，并能预测反应的副产物。
英文摘要
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. We introduce MAELLE (MechAnistic Edit fLow-matching on eLectron rEarrangements), 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 interpolants between the reactants and products, we generalize the discrete flow matching mixture path to an edit-based formulation, where the electron moves are interpolated using Optimal Transport, yielding a mechanism-like set of moves without elementary step annotations. MAELLE achieves competitive performance on the USPTO-480K benchmark compared with leading reaction prediction models. Beyond in-distribution learning, 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.
