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

Abstract 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...

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Bibliographic Details
Main Authors: Miyuki Sakai, Kazuki Nagayasu, Norihiro Shibui, Chihiro Andoh, Kaito Takayama, Hisashi Shirakawa, Shuji Kaneko
Format: article
Language:EN
Published: Nature Portfolio 2021
Subjects:
R
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Online Access:https://doaj.org/article/e24b906189ad43caa5818c58ca25debc
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