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DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.
Li, Fenglei; Hu, Qiaoyu; Zhang, Xianglei; Sun, Renhong; Liu, Zhuanghua; Wu, Sanan; Tian, Siyuan; Ma, Xinyue; Dai, Zhizhuo; Yang, Xiaobao; Gao, Shenghua; Bai, Fang.
Afiliação
  • Li F; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Hu Q; School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Zhang X; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Sun R; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Liu Z; Gluetacs Therapeutics (Shanghai) Co., Ltd., 99 Haike Road, Zhangjiang Hi-Tech Park, Shanghai, 201210, China.
  • Wu S; School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Tian S; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Ma X; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Dai Z; School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Yang X; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Gao S; School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
  • Bai F; School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
Nat Commun ; 13(1): 7133, 2022 11 21.
Article em En | MEDLINE | ID: mdl-36414666
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China