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
in English
| MEDLINE | ID: covidwho-707714
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
Drug Evaluation, Preclinical
/
Molecular Docking Simulation
/
SARS-CoV-2
Type of study:
Prognostic study
Topics:
Traditional medicine
Limits:
Humans
Language:
English
Journal:
Genomics
Journal subject:
Genetics
Year:
2020
Document Type:
Article
Affiliation country:
J.ygeno.2020.07.044
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