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Journal of Medicinal Chemistry
Article
License: CC BY
Data sources: UnpayWall
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PubMed Central
Other literature type . 2020
Data sources: PubMed Central
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Journal of Medicinal Chemistry
Article . 2020 . Peer-reviewed
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Targeting SARS-CoV-2 Proteases and Polymerase for COVID-19 Treatment: State of the Art and Future Opportunities

Authors: Cannalire, Rolando; Cerchia, Carmen; Beccari, Andrea R; Di Leva, Francesco Saverio; Summa, Vincenzo;

Targeting SARS-CoV-2 Proteases and Polymerase for COVID-19 Treatment: State of the Art and Future Opportunities

Abstract

The newly emerged coronavirus, called SARS-CoV-2, is the causing pathogen of pandemic COVID-19. The identification of drugs to treat COVID-19 and other coronavirus diseases is an urgent global need, thus different strategies targeting either virus or host cell are still under investigation. Direct-acting agents, targeting protease and polymerase functionalities, represent a milestone in antiviral therapy. The 3C-like (or Main) protease (3CLpro) and the nsp12 RNA-dependent RNA-polymerase (RdRp) are the best characterized SARS-CoV-2 targets and show the highest degree of conservation across coronaviruses fostering the identification of broad-spectrum inhibitors. Coronaviruses also possess a papain-like protease, another essential enzyme, still poorly characterized and not equally conserved, limiting the identification of broad-spectrum agents. Herein, we provide an exhaustive comparative analysis of SARS-CoV-2 proteases and RdRp with respect to other coronavirus homologues. Moreover, we highlight the most promising inhibitors of these proteins reported so far, including the possible strategies for their further development.

Country
Italy
Keywords

Molecular Structure, SARS-CoV-2, COVID-19, RNA-Dependent RNA Polymerase, Antiviral Agents, COVID-19 Drug Treatment, Drug Discovery, Molecular Medicine, Humans, Protease Inhibitors, Coronavirus 3C Proteases

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    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    198
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 0.1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 0.1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
198
Top 0.1%
Top 10%
Top 0.1%
Green
hybrid