Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction datasets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS and Proparg-21-TS datasets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different datasets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

翻译：几何深度学习模型通过在神经网络架构中融入相关的分子对称性，显著提高了分子性质预测的准确性和数据效率。基于这一成功，我们提出了3DReact——一种用于从反应物和产物的三维结构预测反应性质的几何深度学习模型。我们证明了该模型的不变版本对于现有反应数据集已足够适用。我们在GDB7-22-TS、Cyclo-23-TS和Proparg-21-TS数据集上，针对不同原子映射机制展示了其在活化能垒预测方面的竞争优势。研究表明，与现有的反应性质预测模型相比，3DReact提供了一个灵活框架：在可获得原子映射信息时充分利用该信息，同时结合反应物和产物的几何结构（以不变或等变方式）。因此，该模型在不同数据集、原子映射机制以及内插和外推任务中均表现出系统性的优异性能。