tetano
Editor, Senior Moderator
Curr Drug Targets
. 2023 Mar 6.
doi: 10.2174/1389450124666230306141725. Online ahead of print.
High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior
Zhiwei Yang[SUP] 1 [/SUP], Xinhui Cai[SUP] 1 [/SUP], Qiushi Ye[SUP] 1 [/SUP], Yizhen Zhao[SUP] 1 [/SUP], Xuhua Li[SUP] 1 [/SUP], Shengli Zhang[SUP] 1 [/SUP], Lei Zhang[SUP] 1 [/SUP]
Affiliations
Abstract
Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of high-throughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti-SARS-CoV-2 agents.
Keywords: High-throughput screening; Ligand-based screening; Machine learning-based scoring functions; Receptor-based screening; Structural dynamics.
. 2023 Mar 6.
doi: 10.2174/1389450124666230306141725. Online ahead of print.
High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior
Zhiwei Yang[SUP] 1 [/SUP], Xinhui Cai[SUP] 1 [/SUP], Qiushi Ye[SUP] 1 [/SUP], Yizhen Zhao[SUP] 1 [/SUP], Xuhua Li[SUP] 1 [/SUP], Shengli Zhang[SUP] 1 [/SUP], Lei Zhang[SUP] 1 [/SUP]
Affiliations
- PMID: 36876836
- DOI: 10.2174/1389450124666230306141725
Abstract
Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of high-throughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti-SARS-CoV-2 agents.
Keywords: High-throughput screening; Ligand-based screening; Machine learning-based scoring functions; Receptor-based screening; Structural dynamics.