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10.3389/fchem.2020.590263 http://scihub22266oqcxt.onion/10.3389/fchem.2020.590263 33425850!7786237!33425850     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Chem 2020 ; 8 (ä): 590263 Nephropedia Template TP {{tp|p=33425850|t=2020. Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2 |pdf=|usr=}}{{33425850}} gab.com Text Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2 JO: Front Chem http://www.kidney.de/pget.php?&tw=1&pmid=33425850 #FOAvip The rapidly developing pandemic, known as coronavirus disease 2019 (COVID-19) and caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently spread across 213 countries and territories. This pandemic is a dire public health threat-particularly for those suffering from hypertension, cardiovascular diseases, pulmonary diseases, or diabetes; without approved treatments, it is likely to persist or recur. To facilitate the rapid discovery of inhibitors with clinical potential, we have applied ligand- and structure-based computational approaches to develop a virtual screening methodology that allows us to predict potential inhibitors. In this work, virtual screening was performed against two natural products databases, Super Natural II and Traditional Chinese Medicine. Additionally, we have used an integrated drug repurposing approach to computationally identify potential inhibitors of the main protease of SARS-CoV-2 in databases of drugs (both approved and withdrawn). Roughly 360,000 compounds were screened using various molecular fingerprints and molecular docking methods; of these, 80 docked compounds were evaluated in detail, and the 12 best hits from four datasets were further inspected via molecular dynamics simulations. Finally, toxicity and cytochrome inhibition profiles were computationally analyzed for the selected candidate compounds. Twit Text FOAVip Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2 ????Front Chem http://www.kidney.de/pget.php?&tw=1&pmid=33425850 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33425850 #FOAvip English Wikipedia <ref>{{Cite pmid|33425850}}</ref> Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2 #MMPMID33425850 Abel R; Paredes Ramos M; Chen Q; Perez-Sanchez H; Coluzzi F; Rocco M; Marchetti P; Mura C; Simmaco M; Bourne PE; Preissner R; Banerjee P Front Chem 2020[]; 8 (ä): 590263 PMID33425850show ga The rapidly developing pandemic, known as coronavirus disease 2019 (COVID-19) and caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently spread across 213 countries and territories. This pandemic is a dire public health threat-particularly for those suffering from hypertension, cardiovascular diseases, pulmonary diseases, or diabetes; without approved treatments, it is likely to persist or recur. To facilitate the rapid discovery of inhibitors with clinical potential, we have applied ligand- and structure-based computational approaches to develop a virtual screening methodology that allows us to predict potential inhibitors. In this work, virtual screening was performed against two natural products databases, Super Natural II and Traditional Chinese Medicine. Additionally, we have used an integrated drug repurposing approach to computationally identify potential inhibitors of the main protease of SARS-CoV-2 in databases of drugs (both approved and withdrawn). Roughly 360,000 compounds were screened using various molecular fingerprints and molecular docking methods; of these, 80 docked compounds were evaluated in detail, and the 12 best hits from four datasets were further inspected via molecular dynamics simulations. Finally, toxicity and cytochrome inhibition profiles were computationally analyzed for the selected candidate compounds. äComputational Prediction of Potential Inhibitors of the Main Protease (epmc).pdf DeepDyve Pubget Overpricing