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）几何结构的探索对于阐明化学反应机理及其动力学建模至关重要。近年来，机器学习（ML）模型在预测TS几何结构方面表现出卓越性能。然而，这些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，这需要大量人力投入和计算成本。在此，我们提出一种基于随机扩散方法的生成式方法——TSDiff，该方法仅需二维分子图即可预测TS几何结构。TSDiff在准确性和效率上均优于现有基于三维几何结构的ML模型。此外，由于在训练中学习了不同反应中TS几何结构的分布，该模型能够对多种TS构象进行采样。因此，TSDiff能够比参考数据库找到更优势的反应路径（具有更低势垒高度）。这些结果表明，TSDiff在高效可靠的TS探索方面展现出显著潜力。