Equivariant neural networks have considerably improved the accuracy and data-efficiency of predictions of molecular properties. Building on this success, we introduce EquiReact, an equivariant neural network to infer properties of chemical reactions, built from three-dimensional structures of reactants and products. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS and Proparg-21-TS datasets with different regimes according to the inclusion of atom-mapping information. We show that, compared to state-of-the-art models for reaction property prediction, EquiReact offers: (i) a flexible model with reduced sensitivity between atom-mapping regimes, (ii) better extrapolation capabilities to unseen chemistries, (iii) impressive prediction errors for datasets exhibiting subtle variations in three-dimensional geometries of reactants/products, (iv) reduced sensitivity to geometry quality and (iv) excellent data efficiency.

在化学反应的推理中，我们引入了一个基于三维结构的反应物和生成物构建的等变神经网络EquiReact，展示了它在GDB7-22-TS、Cyclo-23-TS和Proparg-21-TS数据集的活化能预测中的竞争性能，对于原子映射信息的包含程度不同的数据集，相比于最新模型，EquiReact提供了：（i）一个灵活的模型，缓解了原子映射变量之间的敏感性，（ii）更好的对未知化学反应的外推能力，（iii）在反应物/生成物的三维几何变化较为微小的数据集中表现出色的预测误差，（iv）对几何质量的敏感性减少以及（iv）出色的数据效率。

EquiReact: 化学反应的等变神经网络