Name
Abstract | ||
|---|---|---|
The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papainlike protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on Dock-CoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/nar/gkaa861 | NUCLEIC ACIDS RESEARCH |
DocType | Volume | Issue |
Journal | 49 | D1 |
ISSN | Citations | PageRank |
0305-1048 | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ting-Fu Chen | 1 | 0 | 0.34 |
| Yu-Chuan Chang | 2 | 0 | 0.34 |
| Yi Hsiao | 3 | 0 | 0.34 |
| Ko-Han Lee | 4 | 0 | 0.34 |
| Yu-Chun Hsiao | 5 | 0 | 0.34 |
| Yu-Hsiang Lin | 6 | 0 | 0.34 |
| Yi-Chin Ethan Tu | 7 | 0 | 0.34 |
| Hsuan-Cheng Huang | 8 | 0 | 0.68 |
| Chien-Yu Chen | 9 | 367 | 29.24 |
| Hsueh-Fen Juan | 10 | 121 | 9.52 |