3DReact: geometric deep learning for chemical reactions

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 data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets 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 data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

将相关分子对称性融入神经网络架构的几何深度学习（Geometric Deep Learning）模型，已显著提升了分子性质预测的准确性与数据利用效率。在此研究基础上，我们提出了3DREACT——一款可基于反应物与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，该模型的不变性版本即可适配现有反应数据集。我们验证了其在不同原子映射模式下，于GDB7-22-TS、Cyclo-23-TS及Proparg-21-TS数据集上开展活化能垒预测任务时的优异竞争力表现。我们进一步表明，相较于现有的反应性质预测模型，3DREACT提供了一套灵活的框架：若可获取原子映射信息则可加以利用，同时也能以不变性或等变性的方式适配反应物与产物的三维几何结构。因此，该模型在各类数据集、不同原子映射模式下，以及插值与外推任务中均能保持稳定优异的表现。