Finding Plausible Reaction Mechanism Pathways with Learned Heuristic Functions and Post-Hoc Energy Screening

A chemical reaction is composed of a pathway of elementary reaction mechanism steps. Knowledge of the reaction mechanism pathway in a chemical reaction reveal intermediate molecules, allow predictions of similar reactions under different conditions, and validate the feasibility of a reaction. Many existing methods for finding reaction mechanism pathways rely on expert-annotated reaction mechanism data, which are expensive to obtain. We introduce a method that does not rely on any expert data by leveraging an “expansion function” that returns all possible elementary steps for a given set of molecules and the ability to screen implausible elementary steps using a free energy analysis. Using the expansion function, we train a heuristic function that estimates the smallest number of elementary steps needed to reach any given chemical products from any given set of molecules. The expansion function and trained heuristic function are then combined with heuristic search to find reaction mechanism pathways given the reactants and products in a chemical reaction. We repeat the heuristic search with different parameters to obtain multiple possible pathways per reaction. Finally, we use the post-hoc energy screening to screen implausible pathways. Our approach finds pathways for 79.62% of USPTO reactions, 91.71% of Human Benchmark reactions, and 45.11% of ORD reactions. This is a 24.89% and 71.39% improvement in coverage on the Human Benchmark and ORD datasets, respectively, compared to a pathway finding algorithm trained on expert data. While, after post-hoc energy screening, our approach finds between 16.67% and 26.83% fewer plausible pathways compared to the algorithm trained on expert data, it does so without being trained on any expert data, at all. However, it exactly recovers 31 Human Benchmark pathways that were assessed as plausible by experts. In our approach, the energy screen also removes high energy candidates while retaining lower energy alternatives. Together, these results show that the combination of an expansion function and post-hoc energy screening provides a promising alternative to training on expert data for finding reaction mechanism pathways.