Generating transition states of chemical reactions via distance-geometry-based flow matching

TL;DR

This paper introduces TS-DFM, a novel flow matching framework operating in molecular distance geometry space, which accurately predicts transition states of chemical reactions, improving structure accuracy and aiding reaction pathway discovery.

Contribution

The paper presents TS-DFM and TSDVNet, a new method for predicting transition states from reactants and products, outperforming previous methods and enabling discovery of alternative reaction pathways.

Findings

TS-DFM outperforms React-OT by 30% in structural accuracy.

Predicted TSs accelerate convergence of reaction pathway optimization.

TS-DFM generalizes well to unseen molecules and reactions.

Abstract

Transition states (TSs) are crucial for understanding reaction mechanisms, yet their exploration is limited by the complexity of experimental and computational approaches. Here we propose TS-DFM, a flow matching framework that predicts TSs from reactants and products. By operating in molecular distance geometry space, TS-DFM explicitly captures the dynamic changes of interatomic distances in chemical reactions. A network structure named TSDVNet is designed to learn the velocity field for generating TS geometries accurately. On the benchmark dataset Transition1X, TS-DFM outperforms the previous state-of-the-art method React-OT by 30\% in structural accuracy. These predicted TSs provide high-quality initial structures, accelerating the convergence of CI-NEB optimization. Additionally, TS-DFM can identify alternative reaction paths. In our experiments, even a more favorable TS with lower energy barrier is discovered. Further tests on RGD1 dataset confirm its strong generalization ability on unseen molecules and reaction types, highlighting its potential for facilitating reaction exploration.