{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T14:00:33Z","timestamp":1772805633027,"version":"3.50.1"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Institutes of Health, National Institute on Drug Abuse","award":["P30 DA035778A1"],"award-info":[{"award-number":["P30 DA035778A1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.<\/jats:p>","DOI":"10.1093\/bib\/bbaa260","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T19:12:49Z","timestamp":1600110769000},"page":"946-962","source":"Crossref","is-referenced-by-count":22,"title":["MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs"],"prefix":"10.1093","volume":"22","author":[{"given":"Zhiwei","family":"Feng","sequence":"first","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Maozi","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Ying","family":"Xue","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Tianjian","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Yuehan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Thomas D","family":"Nolin","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Randall B","family":"Smith","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Xiang-Qun","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]}],"member":"286","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"2021032314282838400_ref1","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0140-6736(20)30260-9","article-title":"Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling 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