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...

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Detalles Bibliográficos
Autores principales: Kong, Yue, Zhao, Xiaoman, Liu, Ruizi, Yang, Zhenwu, Yin, Hongyan, Zhao, Bowen, Wang, Jinling, Qin, Bingjie, Yan, Aixia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Research Article
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

Internet

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

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