Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method.
Genomics
; 112(6): 4427-4434, 2020 11.
Article
em En
| MEDLINE
| ID: mdl-32745502
It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Antivirais
/
Avaliação Pré-Clínica de Medicamentos
/
Simulação de Acoplamento Molecular
/
SARS-CoV-2
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Genomics
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China