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.

翻译：等变神经网络已显著提升了分子性质预测的准确性与数据效率。基于此进展，我们提出EquiReact——一种从反应物和产物三维结构出发推断化学反应性质的等变神经网络。我们通过包含不同原子映射信息处理方式的GDB7-22-TS、Cyclo-23-TS和Proparg-21-TS数据集，展示了其在活化能垒预测任务上的优异表现。研究表明，与当前最先进的反应性质预测模型相比，EquiReact具备以下优势：(i) 灵活的模型架构，对原子映射模式变化的敏感性较低；(ii) 更优的外推能力，可适应未见化学体系；(iii) 在反应物/产物三维几何结构存在细微差异的数据集上，预测误差表现突出；(iv) 对几何构型质量的鲁棒性；(v) 卓越的数据效率。