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10.1093/bib/bbab211 http://scihub22266oqcxt.onion/10.1093/bib/bbab211 34081143!8195169!34081143     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2021 ; 22 (6): ä Nephropedia Template TP {{tp|p=34081143|t=2021. A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2 |pdf=|usr=}}{{34081143}} gab.com Text A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2 JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34081143 #FOAvip The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. In this study, we used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then, we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19. Twit Text FOAVip A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2 ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34081143 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34081143 #FOAvip English Wikipedia <ref>{{Cite pmid|34081143}}</ref> A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2 #MMPMID34081143 Wang S; Sun Q; Xu Y; Pei J; Lai L Brief Bioinform 2021[Nov]; 22 (6): ä PMID34081143show ga The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. In this study, we used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then, we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19. |*COVID-19 Drug Treatment[MESH] |*Drug Repositioning[MESH] |Antiviral Agents/*chemistry/therapeutic use[MESH] |COVID-19/virology[MESH] |Deep Learning[MESH] |Humans[MESH] |Molecular Docking Simulation[MESH] |Pandemics[MESH]A transferable deep learning approach to fast screen potential antivir(epmc).pdf DeepDyve Pubget Overpricing