Beyond Major Product Prediction: Reproducing Reaction Mechanisms with
  Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Joonyoung F. Joung Mun Hong Fong Jihye Roh Zhengkai Tu John Bradshaw Connor W. Coley
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.