Article ; Online: AI-Driven Discovery of SARS-CoV-2 Main Protease Fragment-like Inhibitors with Antiviral Activity
Journal of chemical information and modeling
2023 Volume 63, Issue 9, Page(s) 2866–2880
Abstract: SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved ...
| Abstract | SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp's, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by |
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| MeSH term(s) | Humans ; Antiviral Agents/pharmacology ; Antiviral Agents/chemistry ; COVID-19 ; SARS-CoV-2 ; Artificial Intelligence ; Protease Inhibitors/pharmacology ; Protease Inhibitors/chemistry ; Molecular Docking Simulation |
| Chemical Substances | Antiviral Agents ; nirmatrelvir and ritonavir drug combination ; 3C-like proteinase, SARS-CoV-2 (EC 3.4.22.-) ; Protease Inhibitors |
| Language | English |
| Publishing date | 2023-04-14 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 190019-5 |
| ISSN | 1549-960X ; 0095-2338 |
| ISSN (online) | 1549-960X |
| ISSN | 0095-2338 |
| DOI | 10.1021/acs.jcim.3c00409 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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