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10.1080/07391102.2021.1902399 http://scihub22266oqcxt.onion/10.1080/07391102.2021.1902399 33749545!ä!33749545 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomol+Struct+Dyn 2022 ; 40 (17): 7815-7828 Nephropedia Template TP {{tp|p=33749545|t=2022. On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics |pdf=|usr=}}{{33749545}} gab.com Text On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics JO: J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=33749545 #FOAvip COVID-19 also known as SARS-CoV-2 outbreak in late 2019 and its worldwide pandemic spread has taken the world by surprise. The minute-to-minute increasing coronavirus cases (>85 M) and progressive deaths ( approximately 1.8 M) calls for finding a cure to this devastating pandemic. While there have been many attempts to find biologically active molecules targeting SARS-CoV-2 for treatment of this viral infection, none has found a way to the clinic yet. In this study, a 3-feature structure-based pharmacophore model was designed for SARS-CoV-2 main protease (M(Pro)) that plays a vital role in the viral cellular penetration. High throughput virtual screening of the lead-like ZINC library was then performed to find a potent inhibitor employing the predesigned pharmacophore. In-silico pharmacokinetics/toxicity prediction study was subsequently applied towards the best hits. Finally, a 50 ns molecular dynamics simulation was carried out for the best hit and compared to the co-crystallized ligand where the hit compound displayed high binding and comparable interactions. The results identified new hits for SARS-CoV-2 M(Pro) inhibition showing good docking score, pharmacokinetics and toxicity profile, drug-likeness, high binding energy in addition to a promising synthetic accessibility. Identifying new small compounds as potential leads for inhibiting SARS-CoV-2 is a very important step towards designing a synthesizing of promising drug candidates.Communicated by Ramaswamy H. Sarma. Twit Text FOAVip On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics ????J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=33749545 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33749545 #FOAvip English Wikipedia <ref>{{Cite pmid|33749545}}</ref> On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics #MMPMID33749545 Hassan A; Arafa RK J Biomol Struct Dyn 2022[Oct]; 40 (17): 7815-7828 PMID33749545show ga COVID-19 also known as SARS-CoV-2 outbreak in late 2019 and its worldwide pandemic spread has taken the world by surprise. The minute-to-minute increasing coronavirus cases (>85 M) and progressive deaths ( approximately 1.8 M) calls for finding a cure to this devastating pandemic. While there have been many attempts to find biologically active molecules targeting SARS-CoV-2 for treatment of this viral infection, none has found a way to the clinic yet. In this study, a 3-feature structure-based pharmacophore model was designed for SARS-CoV-2 main protease (M(Pro)) that plays a vital role in the viral cellular penetration. High throughput virtual screening of the lead-like ZINC library was then performed to find a potent inhibitor employing the predesigned pharmacophore. In-silico pharmacokinetics/toxicity prediction study was subsequently applied towards the best hits. Finally, a 50 ns molecular dynamics simulation was carried out for the best hit and compared to the co-crystallized ligand where the hit compound displayed high binding and comparable interactions. The results identified new hits for SARS-CoV-2 M(Pro) inhibition showing good docking score, pharmacokinetics and toxicity profile, drug-likeness, high binding energy in addition to a promising synthetic accessibility. Identifying new small compounds as potential leads for inhibiting SARS-CoV-2 is a very important step towards designing a synthesizing of promising drug candidates.Communicated by Ramaswamy H. Sarma. |*COVID-19 Drug Treatment[MESH] |Coronavirus 3C Proteases[MESH] |Cysteine Endopeptidases/chemistry[MESH] |Humans[MESH] |Ligands[MESH] |Molecular Docking Simulation[MESH] |Molecular Dynamics Simulation[MESH] |Protease Inhibitors/chemistry/pharmacology[MESH] |SARS-CoV-2[MESH]On the search for COVID-19 therapeutics: identification of potential S(epmc).pdf DeepDyve Pubget Overpricing