Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

Nguyen, Trung Hai; Thai, Quynh Mai; Pham, Minh Quan; Minh, Pham Thi Hong; Phung, Huong Thi Thu

Nguyen, Trung Hai; Thai, Quynh Mai; Pham, Minh Quan; Minh, Pham Thi Hong; Phung, Huong Thi Thu.

Afiliação

Nguyen TH; Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Thai QM; Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Pham MQ; Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Minh PTH; Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam.
Phung HTT; Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam.

Mol Divers ; 2023 Feb 24.

Article em En | MEDLINE | ID: mdl-36823394

Palavras-chave

Inhibitors; Linear interaction energy; Machine learning; Molecular docking; Molecular dynamic simulations; Natural compounds; SARS-CoV-2 Mpro

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Divers Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Vietnã

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google