Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs
This work addresses the challenge of efficient transition state exploration for chemists and computational researchers, offering a novel generative approach that reduces input requirements and computational costs.
The paper tackled the problem of predicting transition state geometries for chemical reactions, which traditionally requires costly 3D inputs, by proposing TSDiff, a diffusion-based generative model that uses only 2D molecular graphs. TSDiff outperformed existing ML models in accuracy and efficiency, enabling the discovery of more favorable reaction pathways with lower barrier heights.
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperformed the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learned the distribution of TS geometries for diverse reactions in training. Thus, TSDiff was able to find more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.