Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.08307 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917755420147712 |
|---|---|
| author | van Gerwen, Puck Briling, Ksenia R. Bunne, Charlotte Somnath, Vignesh Ram Laplaza, Ruben Krause, Andreas Corminboeuf, Clemence |
| author_facet | van Gerwen, Puck Briling, Ksenia R. Bunne, Charlotte Somnath, Vignesh Ram Laplaza, Ruben Krause, Andreas Corminboeuf, Clemence |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08307 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 3DReact: Geometric deep learning for chemical reactions van Gerwen, Puck Briling, Ksenia R. Bunne, Charlotte Somnath, Vignesh Ram Laplaza, Ruben Krause, Andreas Corminboeuf, Clemence Chemical Physics Machine Learning 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. |
| title | 3DReact: Geometric deep learning for chemical reactions |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2312.08307 |