iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Liu, Xiao-Wei; Shi, Tian-Yu; Gao, Dong; Ma, Cai-Yi; Lin, Hao; Yan, Dan; Deng, Ke-Jun

Liu, Xiao-Wei; Shi, Tian-Yu; Gao, Dong; Ma, Cai-Yi; Lin, Hao; Yan, Dan; Deng, Ke-Jun.

Afiliação

Liu XW; School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Shi TY; School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Gao D; School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Ma CY; School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Lin H; School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Yan D; Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Deng KJ; Beijing Institute of Clinical Pharmacy, Beijing 100050, China.

J Chem Inf Model ; 63(15): 4960-4969, 2023 08 14.

Article em En | MEDLINE | ID: mdl-37499224

Assuntos

Inteligência Artificial; Hipoglicemiantes; Hipoglicemiantes/farmacologia; Simulação de Acoplamento Molecular; Algoritmos; Aprendizado de Máquina

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Hipoglicemiantes Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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