3DReact: Geometric deep learning for chemical reactions

• URL: http://arxiv.org/abs/2312.08307v2
• Date: Fri, 12 Jul 2024 14:15:23 GMT
• Title: 3DReact: Geometric deep learning for chemical reactions
• Authors: Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Ruben Laplaza, Andreas Krause, Clemence Corminboeuf,
• Abstract summary: We introduce 3DReact, a deep geometric learning model to predict reaction properties from three-dimensional structures of reactants and products. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information. It performs systematically well across different datasets, atom-mapping regimes, as well as both geometries and extrapolation tasks.
• Score: 35.38031930589095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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