PF-07321332 for COVID-19
PF-07321332 has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
Current state-of-the-art and potential future therapeutic drugs against COVID-19, Frontiers in Cell and Developmental Biology, doi:10.3389/fcell.2023.1238027 ,
The novel coronavirus disease (COVID-19) continues to endanger human health, and its therapeutic drugs are under intensive research and development. Identifying the efficacy and toxicity of drugs in animal models is helpful for further screening of effective medications, which is also a prerequisite for drugs to enter clinical trials. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) invades host cells mainly by the S protein on its surface. After the SARS-CoV-2 RNA genome is injected into the cells, M protein will help assemble and release new viruses. RdRp is crucial for virus replication, assembly, and release of new virus particles. This review analyzes and discusses 26 anti-SARS-CoV-2 drugs based on their mechanism of action, effectiveness and safety in different animal models. We propose five drugs to be the most promising to enter the next stage of clinical trial research, thus providing a reference for future drug development.
Repurposing clinically available drugs and therapies for pathogenic targets to combat SARS‐CoV‐2, MedComm, doi:10.1002/mco2.254 ,
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.
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