Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds...

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Detalles Bibliográficos
Autores principales: Sakai, Miyuki, Nagayasu, Kazuki, Shibui, Norihiro, Andoh, Chihiro, Takayama, Kaito, Shirakawa, Hisashi, Kaneko, Shuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803991/
https://www.ncbi.nlm.nih.gov/pubmed/33436854
http://dx.doi.org/10.1038/s41598-020-80113-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803991/
https://www.ncbi.nlm.nih.gov/pubmed/33436854
http://dx.doi.org/10.1038/s41598-020-80113-7

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