Telaprevir for COVID-19
Telaprevir has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics, Molecular Aspects of Medicine, doi:10.1016/j.mam.2022.101151 ,
A drug repurposing screen identifies hepatitis C antivirals as inhibitors of the SARS-CoV2 main protease, PLOS ONE, doi:10.1371/journal.pone.0245962 ,
Effective SARS-CoV-2 antiviral drugs are desperately needed. The SARS-CoV-2 main protease (Mpro) appears as an attractive target for drug development. We show that the existing pharmacopeia contains many drugs with potential for therapeutic repurposing as selective and potent inhibitors of SARS-CoV-2 Mpro. We screened a collection of ~6,070 drugs with a previous history of use in humans for compounds that inhibit the activity of Mpro in vitro and found ~50 compounds with activity against Mpro. Subsequent dose validation studies demonstrated 8 dose responsive hits with an IC50 ≤ 50 μM. Hits from our screen are enriched with hepatitis C NS3/4A protease targeting drugs including boceprevir, ciluprevir. narlaprevir, and telaprevir. This work suggests previous large-scale commercial drug development initiatives targeting hepatitis C NS3/4A viral protease should be revisited because some previous lead compounds may be more potent against SARS-CoV-2 Mpro than boceprevir and suitable for rapid repurposing.
Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy, Briefings in Bioinformatics, doi:10.1093/bib/bbac628 ,
Abstract Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.
Repurposing of HIV/HCV protease inhibitors against SARS-CoV-2 3CLpro, Antiviral Research, doi:10.1016/j.antiviral.2022.105419 ,
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