HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction.

Han, Shen; Fu, Haitao; Wu, Yuyang; Zhao, Ganglan; Song, Zhenyu; Huang, Feng; Zhang, Zhongfei; Liu, Shichao; Zhang, Wen

Han, Shen; Fu, Haitao; Wu, Yuyang; Zhao, Ganglan; Song, Zhenyu; Huang, Feng; Zhang, Zhongfei; Liu, Shichao; Zhang, Wen.

Afiliación

Han S; College of Informatics, Huazhong Agricultural University, People's Republic of China.
Fu H; College of Informatics, Huazhong Agricultural University, People's Republic of China.
Wu Y; College of Plant Science and Technology, Huazhong Agricultural University, People's Republic of China.
Zhao G; College of Informatics, Huazhong Agricultural University, People's Republic of China.
Song Z; College of Informatics, Huazhong Agricultural University, People's Republic of China.
Huang F; College of Informatics, Huazhong Agricultural University, People's Republic of China.
Zhang Z; Computer Science Department, Binghamton University, Binghamton, NY, USA.
Liu S; College of Informatics, Huazhong Agricultural University, People's Republic of China and Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture, Hu
Zhang W; College of Informatics, Huazhong Agricultural University, People's Republic of China and Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture, Hu

Brief Bioinform ; 24(5)2023 09 20.

Article en En | MEDLINE | ID: mdl-37594313

Asunto(s)

Aprendizaje Profundo; Redes Neurales de la Computación; Aprendizaje; Descubrimiento de Drogas

Palabras clave

deep learning; drug discovery; graph neural networks; molecular property prediction

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article

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