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10.2174/0929867328666210113170222 http://scihub22266oqcxt.onion/10.2174/0929867328666210113170222 33441063!ä!33441063 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33441063&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 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Med+Chem 2021 ; 28 (28): 5699-5732 Nephropedia Template TP {{tp|p=33441063|t=2021. Deep Learning in the Quest for Compound Nomination for Fighting COVID-19 |pdf=|usr=}}{{33441063}} gab.com Text Deep Learning in the Quest for Compound Nomination for Fighting COVID-19 JO: Curr Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33441063 #FOAvip The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental to make sense of the data and build models used in prediction-validation workflows that in a matter of months have already produced results in assessing the spread of the outbreak, its taxonomy, population susceptibility, diagnostics or drug discovery and repurposing. More is expected to come in the near future by using such advanced machine learning techniques to combat this pandemic. This review aims to unravel just a small fraction of the large global endeavors by focusing on the research performed on the main COVID-19 targets, on the computational weaponry used in identifying drugs to combat the disease, and on some of the most important directions found to contain COVID-19 or alleviating its symptoms in the absence of specific medication. Twit Text FOAVip Deep Learning in the Quest for Compound Nomination for Fighting COVID-19 ????Curr Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33441063 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33441063 #FOAvip English Wikipedia <ref>{{Cite pmid|33441063}}</ref> Deep Learning in the Quest for Compound Nomination for Fighting COVID-19 #MMPMID33441063 Mernea M; Martin EC; Petrescu AJ; Avram S Curr Med Chem 2021[]; 28 (28): 5699-5732 PMID33441063show ga The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental to make sense of the data and build models used in prediction-validation workflows that in a matter of months have already produced results in assessing the spread of the outbreak, its taxonomy, population susceptibility, diagnostics or drug discovery and repurposing. More is expected to come in the near future by using such advanced machine learning techniques to combat this pandemic. This review aims to unravel just a small fraction of the large global endeavors by focusing on the research performed on the main COVID-19 targets, on the computational weaponry used in identifying drugs to combat the disease, and on some of the most important directions found to contain COVID-19 or alleviating its symptoms in the absence of specific medication. |*COVID-19[MESH] |*Deep Learning[MESH] |Drug Repositioning[MESH] |Humans[MESH] |Pandemics[MESH]Deep Learning in the Quest for Compound Nomination for Fighting COVID-(epmc).pdf DeepDyve Pubget Overpricing