Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
National Center for Competence in Research - Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
J Chem Inf Model. 2024 Aug 12;64(15):5771-5785. doi: 10.1021/acs.jcim.4c00104. Epub 2024 Jul 15.
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.
几何深度学习模型在神经网络架构中纳入了相关的分子对称性，极大地提高了分子性质预测的准确性和数据效率。在此成功的基础上，我们引入了 3DReact，这是一种几何深度学习模型，可根据反应物和产物的三维结构预测反应性质。我们证明了模型的不变版本对于现有的反应数据集是足够的。我们在不同的原子映射规则下，在 GDB7-22-TS、Cyclo-23-TS 和 Proparg-21-TS 数据集上展示了其在预测活化能垒方面的竞争性能。我们表明，与现有的反应性质预测模型相比，3DReact 提供了一个灵活的框架，如果有原子映射信息可用，则可以利用原子映射信息，以及反应物和产物的几何形状（以不变或等变的方式）。因此，它在不同的数据集、原子映射规则以及内插和外推任务中都表现出了系统性的优异性能。
