Force field-inspired molecular representation learning for property prediction
Abstract Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-02-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-023-00691-2 |