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10.1021/acs.jcim.1c00184 http://scihub22266oqcxt.onion/10.1021/acs.jcim.1c00184 33784094!ä!33784094 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Chem+Inf+Model 2021 ; 61 (4): 2062-2073 Nephropedia Template TP {{tp|p=33784094|t=2021. Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening |pdf=|usr=}}{{33784094}} gab.com Text Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening JO: J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=33784094 #FOAvip During almost all 2020, coronavirus disease 2019 (COVID-19) pandemic has constituted the major risk for the worldwide health and economy, propelling unprecedented efforts to discover drugs for its prevention and cure. At the end of the year, these efforts have culminated with the approval of vaccines by the American Food and Drug Administration (FDA) and the European Medicines Agency (EMA) giving new hope for the future. On the other hand, clinical data underscore the urgent need for effective drugs to treat COVID-19 patients. In this work, we embarked on a virtual screening campaign against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M(pro) chymotrypsin-like cysteine protease employing our in-house database of peptide and non-peptide ligands characterized by different types of warheads acting as Michael acceptors. To this end, we employed the AutoDock4 docking software customized to predict the formation of a covalent adduct with the target protein. In vitro verification of the inhibition properties of the most promising candidates allowed us to identify two new lead inhibitors that will deserve further optimization. From the computational point of view, this work demonstrates the predictive power of AutoDock4 and suggests its application for the in silico screening of large chemical libraries of potential covalent binders against the SARS-CoV-2 M(pro) enzyme. Twit Text FOAVip Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening ????J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=33784094 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33784094 #FOAvip English Wikipedia <ref>{{Cite pmid|33784094}}</ref> Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening #MMPMID33784094 Amendola G; Ettari R; Previti S; Di Chio C; Messere A; Di Maro S; Hammerschmidt SJ; Zimmer C; Zimmermann RA; Schirmeister T; Zappala M; Cosconati S J Chem Inf Model 2021[Apr]; 61 (4): 2062-2073 PMID33784094show ga During almost all 2020, coronavirus disease 2019 (COVID-19) pandemic has constituted the major risk for the worldwide health and economy, propelling unprecedented efforts to discover drugs for its prevention and cure. At the end of the year, these efforts have culminated with the approval of vaccines by the American Food and Drug Administration (FDA) and the European Medicines Agency (EMA) giving new hope for the future. On the other hand, clinical data underscore the urgent need for effective drugs to treat COVID-19 patients. In this work, we embarked on a virtual screening campaign against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M(pro) chymotrypsin-like cysteine protease employing our in-house database of peptide and non-peptide ligands characterized by different types of warheads acting as Michael acceptors. To this end, we employed the AutoDock4 docking software customized to predict the formation of a covalent adduct with the target protein. In vitro verification of the inhibition properties of the most promising candidates allowed us to identify two new lead inhibitors that will deserve further optimization. From the computational point of view, this work demonstrates the predictive power of AutoDock4 and suggests its application for the in silico screening of large chemical libraries of potential covalent binders against the SARS-CoV-2 M(pro) enzyme. |*COVID-19[MESH] |*Protease Inhibitors/pharmacology[MESH] |Antiviral Agents/pharmacology[MESH] |Humans[MESH] |Molecular Docking Simulation[MESH] |Pandemics[MESH]Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent(epmc).pdf DeepDyve Pubget Overpricing