Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.

翻译：有机反应的机理理解有助于促进反应开发、杂质预测，并在原理上推动反应发现。尽管已有多种机器学习模型致力于预测反应产物，但由于缺乏相应的机理数据集，这些模型在预测反应机理方面的应用受到阻碍。在本研究中，我们通过使用专家反应模板在实验报道的反应物与产物之间插补中间体，构建了这样一个数据集，并在由此产生的5,184,184个基元步骤数据集上训练了多种机器学习模型。我们探讨了这些模型的性能与能力，重点关注其预测反应路径和重现催化剂及试剂作用的能力。此外，我们展示了机理模型在预测杂质方面的潜力，而传统模型往往忽略这一点。最后，我们评估了机理模型对新反应类型的泛化能力，揭示了与数据集多样性、连续预测及违反原子守恒相关的挑战。