Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime.

Li, Chuan; Wang, Chenghui; Sun, Ming; Zeng, Yan; Yuan, Yuan; Gou, Qiaolin; Wang, Guangchuan; Guo, Yanzhi; Pu, Xuemei

Li, Chuan; Wang, Chenghui; Sun, Ming; Zeng, Yan; Yuan, Yuan; Gou, Qiaolin; Wang, Guangchuan; Guo, Yanzhi; Pu, Xuemei.

Afiliação

Li C; College of Computer Science, Sichuan University, Chengdu610064, China.
Wang C; College of Computer Science, Sichuan University, Chengdu610064, China.
Sun M; College of Chemistry, Sichuan University, Chengdu610064, China.
Zeng Y; College of Computer Science, Sichuan University, Chengdu610064, China.
Yuan Y; College of Management, Southwest University for Nationalities, Chengdu610041, China.
Gou Q; College of Chemistry, Sichuan University, Chengdu610064, China.
Wang G; College of Computer Science, Sichuan University, Chengdu610064, China.
Guo Y; College of Chemistry, Sichuan University, Chengdu610064, China.
Pu X; College of Chemistry, Sichuan University, Chengdu610064, China.

J Chem Inf Model ; 62(20): 4873-4887, 2022 Oct 24.

Article em En | MEDLINE | ID: mdl-35998331

Assuntos

Substâncias Explosivas; Redes Neurais de Computação; Substâncias Explosivas/química; Software

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Substâncias Explosivas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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