http://scholars.ntou.edu.tw/handle/123456789/19011
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yifei Wu | en_US |
dc.contributor.author | Kuan Y. Chang | en_US |
dc.contributor.author | Lei Lou | en_US |
dc.contributor.author | Lorette G. Edwards | en_US |
dc.contributor.author | Bly K. Doma | en_US |
dc.contributor.author | Zhong-Ru Xie | en_US |
dc.date.accessioned | 2021-12-09T06:34:28Z | - |
dc.date.available | 2021-12-09T06:34:28Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://scholars.ntou.edu.tw/handle/123456789/19011 | - |
dc.description.abstract | The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (Mpro), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 Mpro. Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of Mpro. Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for Mpro. Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ELSEVIER | en_US |
dc.relation.ispartof | Informatics in Medicine Unlocked | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | Ligand-protein docking | en_US |
dc.subject | Virtual screening | en_US |
dc.subject | Remdesivir | en_US |
dc.subject | Drug repurposing | en_US |
dc.subject | Main protease | en_US |
dc.subject | RNA-dependent RNA polymerase | en_US |
dc.title | In silico identification of drug candidates against COVID-19 | en_US |
dc.type | journal article | en_US |
dc.identifier.doi | 10.1016/j.imu.2020.100461 | - |
dc.relation.journalvolume | 21 | en_US |
item.cerifentitytype | Publications | - |
item.openairetype | journal article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en_US | - |
crisitem.author.dept | College of Electrical Engineering and Computer Science | - |
crisitem.author.dept | Department of Computer Science and Engineering | - |
crisitem.author.dept | National Taiwan Ocean University,NTOU | - |
crisitem.author.orcid | 0000-0002-2262-5218 | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
Appears in Collections: | 資訊工程學系 |
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