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Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation
Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completel...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351086/ https://www.ncbi.nlm.nih.gov/pubmed/35927691 http://dx.doi.org/10.1186/s13321-022-00634-3 |