Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method

Liqian Zhou, Juanjuan Wang, Guangyi Liu, Qingqing Lu, Ruyi Dong, Geng Tian, Jialiang Yang, Lihong Peng

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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.

Original languageEnglish
Pages (from-to)4427-4434
Number of pages8
JournalGenomics
Volume112
Issue number6
DOIs
StatePublished - Nov 2020
Externally publishedYes

Keywords

  • Antiviral drug
  • Molecular docking
  • SARS-CoV-2
  • VDA
  • VDA-KATZ

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