@article{0ed7138f80794c9aa723be7d3cc2347f,
title = "Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method",
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 = "Antiviral drug, Molecular docking, SARS-CoV-2, VDA, VDA-KATZ",
author = "Liqian Zhou and Juanjuan Wang and Guangyi Liu and Qingqing Lu and Ruyi Dong and Geng Tian and Jialiang Yang and Lihong Peng",
note = "Funding Information: This work was supported by the National Natural Science Foundation of China (Grant 61803151), the Natural Science Foundation of Hunan province (Grant 2018JJ2461, 2018JJ3570). We are thankful for help from Ming Kuang, and Longjie Liao from Hunan University of Technology, Lebin Liang and Jidong Lang from Geneis (Beijing) Co. Ltd. and Junlin Xu from Hunan University. We would like to thank all authors of the cited references. Funding Information: This work was supported by the National Natural Science Foundation of China (Grant 61803151 ), the Natural Science Foundation of Hunan province (Grant 2018JJ2461 , 2018JJ3570 ). We are thankful for help from Ming Kuang, and Longjie Liao from Hunan University of Technology, Lebin Liang and Jidong Lang from Geneis (Beijing) Co. Ltd., and Junlin Xu from Hunan University. We would like to thank all authors of the cited references. Publisher Copyright: {\textcopyright} 2020",
year = "2020",
month = nov,
doi = "10.1016/j.ygeno.2020.07.044",
language = "English",
volume = "112",
pages = "4427--4434",
journal = "Genomics",
issn = "0888-7543",
publisher = "Academic Press Inc.",
number = "6",
}