Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening.

Zhang, Li-Chuan; Zhao, Hui-Lin; Liu, Jin; He, Lei; Yu, Ri-Lei; Kang, Cong-Min

Zhang, Li-Chuan; Zhao, Hui-Lin; Liu, Jin; He, Lei; Yu, Ri-Lei; Kang, Cong-Min.

Afiliação

Zhang LC; College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, 266042, China.
Zhao HL; College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, 266042, China.
Liu J; College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, 266042, China.
He L; College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, 266042, China.
Yu RL; Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine & Pharmacy, Ocean University of China, Qingdao, 266003, China.
Kang CM; College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, 266042, China.

Future Med Chem ; 14(6): 393-405, 2022 03.

Article em En | MEDLINE | ID: mdl-35220726

Assuntos

Antivirais/farmacologia; Aprendizado Profundo; Avaliação Pré-Clínica de Medicamentos; SARS-CoV-2/efeitos dos fármacos; Humanos; Simulação de Acoplamento Molecular; Simulação de Dinâmica Molecular; Inibidores de Proteases/farmacologia

Palavras-chave

Mpro; PLpro; covalent docking; deep reinforcement learning; drug design; molecular dynamics simulation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Antivirais / Avaliação Pré-Clínica de Medicamentos / Aprendizado Profundo / SARS-CoV-2 Tipo de estudo: Diagnostic_studies / Screening_studies Limite: Humans Idioma: En Revista: Future Med Chem Ano de publicação: 2022 Tipo de documento: Article

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