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10.1021/acs.jmedchem.1c01372 http://scihub22266oqcxt.onion/10.1021/acs.jmedchem.1c01372 35275639!8936051!35275639     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=35275639&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       J+Med+Chem 2022 ; 65 (6): 4590-4599 Nephropedia Template TP {{tp|p=35275639|t=2022. Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling |pdf=|usr=}}{{35275639}} gab.com Text Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling JO: J Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=35275639 #FOAvip Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 muM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery. Twit Text FOAVip Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling ????J Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=35275639 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35275639 #FOAvip English Wikipedia <ref>{{Cite pmid|35275639}}</ref> Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling #MMPMID35275639 Xu T; Xu M; Zhu W; Chen CZ; Zhang Q; Zheng W; Huang R J Med Chem 2022[Mar]; 65 (6): 4590-4599 PMID35275639show ga Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 muM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery. |*COVID-19 Drug Treatment[MESH] |*SARS-CoV-2[MESH] |Antiviral Agents/pharmacology[MESH] |Drug Repositioning[MESH] |Humans[MESH]Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical S(epmc).pdf DeepDyve Pubget Overpricing