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10.1021/acs.jpclett.0c01579 http://scihub22266oqcxt.onion/10.1021/acs.jpclett.0c01579 C7313673!7313673 !32543196     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32543196 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32543196 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Phys+Chem+Lett 2020 ; 11 (13 ): 5373-5382 Nephropedia Template TP {{tp|p=32543196 |t=2020. Repositioning of 8565 Existing Drugs for COVID-19 |pdf=|usr=}}{{32543196 }} gab.com Text Repositioning of 8565 Existing Drugs for COVID-19 JO: J Phys Chem Lett http://www.kidney.de/pget.php?&tw=1&pmid=32543196 #FOAvip The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 7.1 million people and led to over 0.4 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than 10 years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, we develop validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 3CL protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning. Twit Text FOAVip Repositioning of 8565 Existing Drugs for COVID-19 ????J Phys Chem Lett http://www.kidney.de/pget.php?&tw=1&pmid=32543196 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32543196 #FOAvip English Wikipedia <ref>{{Cite pmid|32543196 }}</ref> Repositioning of 8565 Existing Drugs for COVID-19 #MMPMID32543196 Gao K ; Nguyen DD ; Chen J ; Wang R ; Wei GW J Phys Chem Lett 2020[Jul]; 11 (13 ): 5373-5382 PMID32543196 show ga The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 7.1 million people and led to over 0.4 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than 10 years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, we develop validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 3CL protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning. |*Drug Repositioning [MESH] |Antiviral Agents/*metabolism [MESH] |Betacoronavirus/enzymology [MESH] |COVID-19 [MESH] |COVID-19 Drug Treatment [MESH] |Coronavirus 3C Proteases [MESH] |Coronavirus Infections/*drug therapy/enzymology [MESH] |Cysteine Endopeptidases/metabolism [MESH] |Cysteine Proteinase Inhibitors/*metabolism [MESH] |Databases, Protein/statistics & numerical data [MESH] |Humans [MESH] |Machine Learning [MESH] |Pandemics [MESH] |Pneumonia, Viral/*drug therapy/enzymology [MESH] |Protein Binding [MESH] |SARS-CoV-2 [MESH]Repositioning of 8565 Existing Drugs for COVID-19 (epmc).pdf DeepDyve Pubget Overpricing