Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learning
This addresses a fundamental challenge in chemistry for efficient synthetic pathway design, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of predicting chemical reaction outcomes and byproducts by introducing Reactron, the first electron-based machine learning model that integrates electron movements to generate mechanistic diagrams. It demonstrates high predictive performance on benchmarks and adaptability to learn new reactivities with few examples.
Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design.