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.

翻译：过渡态（TS）几何构型的探索对于阐明化学反应机理及模拟其动力学至关重要。近年来，机器学习模型在预测过渡态几何构型方面展现出卓越性能，但这类模型通常需要反应物和产物的三维构象及其适当取向作为输入，这需要大量计算投入和人力成本。本文提出一种基于随机扩散方法的生成式方法TSDiff，仅需二维分子图即可预测过渡态几何构型。TSDiff在准确性和效率两方面均超越了基于三维几何构型的现有机器学习模型。此外，由于在训练过程中学习了各类反应的过渡态几何分布，该模型能够对多种过渡态构象进行采样。实验表明，TSDiff能发现比参考数据库中更优的低能垒反应路径。上述结果证明TSDiff在实现高效可靠的过渡态探索方面具有重要应用潜力。