Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learning

2025-03-13

AI Summary

This work addresses the challenge of accurately predicting both primary reaction outcomes and side products to enhance synthetic route design. We propose Reactron—the first general-purpose reaction prediction model that explicitly models electron movement. Methodologically, Reactron (1) encodes electron transfer as a primary graph neural network representation via an electron trajectory encoder and mechanism-aware attention; (2) generates interpretable reaction pathways grounded in arrow-pushing rules, enabling joint prediction of major and minor products; and (3) supports few-shot transfer learning and combinatorial exploration of reaction space, conferring out-of-distribution generalization and capacity for discovering novel reactivity. Evaluated on large-scale benchmarks, Reactron substantially outperforms state-of-the-art methods and successfully predicts previously unreported reaction pathways, demonstrating strong robustness and significant potential for scientific discovery.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Predicting chemical reaction outcomes using electron movements.
• Enhancing synthetic pathway design through accurate reaction prediction.
• Exploring novel reactivities beyond existing training data.

Innovation

• Electron-based machine learning for reaction prediction
• Generates detailed arrow-pushing diagrams for mechanisms
• Explores novel reactivities beyond training data