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10.3390/molecules26061549 http://scihub22266oqcxt.onion/10.3390/molecules26061549 33799871!8000608!33799871     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Molecules 2021 ; 26 (6): ä Nephropedia Template TP {{tp|p=33799871|t=2021. Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 M(pro) |pdf=|usr=}}{{33799871}} gab.com Text Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 M(pro) JO: Molecules http://www.kidney.de/pget.php?&tw=1&pmid=33799871 #FOAvip Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (M(pro)) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski's filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as M(pro) inhibitors and potential treatment options for COVID-19, bench work investigations are needed. Twit Text FOAVip Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 M(pro) ????Molecules http://www.kidney.de/pget.php?&tw=1&pmid=33799871 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33799871 #FOAvip English Wikipedia <ref>{{Cite pmid|33799871}}</ref> Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 M(pro) #MMPMID33799871 Saeed M; Saeed A; Alam MJ; Alreshidi M Molecules 2021[Mar]; 26 (6): ä PMID33799871show ga Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (M(pro)) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski's filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as M(pro) inhibitors and potential treatment options for COVID-19, bench work investigations are needed. |*COVID-19 Drug Treatment[MESH] |Antiviral Agents/*chemistry/*pharmacology[MESH] |Binding Sites[MESH] |Biological Products/*chemistry/*pharmacology[MESH] |Coronavirus 3C Proteases/antagonists & inhibitors/chemistry/metabolism[MESH] |Databases, Pharmaceutical[MESH] |Drug Discovery[MESH] |Humans[MESH] |Hydrogen Bonding[MESH] |Hydrophobic and Hydrophilic Interactions[MESH] |Ligands[MESH] |Molecular Docking Simulation[MESH] |Molecular Dynamics Simulation[MESH] |Protein Binding[MESH] |Quantitative Structure-Activity Relationship[MESH]Receptor-Based Pharmacophore Modeling in the Search for Natural Produc(epmc).pdf DeepDyve Pubget Overpricing