Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking
Abstract Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that a...
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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Nature Portfolio
2023-05-01
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| Schriftenreihe: | Nature Communications |
| Online-Zugang: | https://doi.org/10.1038/s41467-023-38192-3 |
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