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10.1016/j.bj.2020.05.001 http://scihub22266oqcxt.onion/10.1016/j.bj.2020.05.001 32426387!7227517!32426387     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+J 2020 ; 43 (4): 355-362 Nephropedia Template TP {{tp|p=32426387|t=2020. Artificial intelligence approach fighting COVID-19 with repurposing drugs |pdf=|usr=}}{{32426387}} gab.com Text Artificial intelligence approach fighting COVID-19 with repurposing drugs JO: Biomed J http://www.kidney.de/pget.php?&tw=1&pmid=32426387 #FOAvip BACKGROUND: The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19. METHODS: An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in in vitro cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again. RESULTS: After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed in vitro activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches. CONCLUSION: Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 in vitro and in vivo at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic. Twit Text FOAVip Artificial intelligence approach fighting COVID-19 with repurposing drugs ????Biomed J http://www.kidney.de/pget.php?&tw=1&pmid=32426387 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32426387 #FOAvip English Wikipedia <ref>{{Cite pmid|32426387}}</ref> Artificial intelligence approach fighting COVID-19 with repurposing drugs #MMPMID32426387 Ke YY; Peng TT; Yeh TK; Huang WZ; Chang SE; Wu SH; Hung HC; Hsu TA; Lee SJ; Song JS; Lin WH; Chiang TJ; Lin JH; Sytwu HK; Chen CT Biomed J 2020[Aug]; 43 (4): 355-362 PMID32426387show ga BACKGROUND: The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19. METHODS: An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in in vitro cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again. RESULTS: After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed in vitro activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches. CONCLUSION: Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 in vitro and in vivo at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic. |*Artificial Intelligence[MESH] |*Drug Repositioning[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnosis/*drug therapy[MESH] |Data Management[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/*drug therapy[MESH] |Predictive Value of Tests[MESH]Artificial intelligence approach fighting COVID-19 with repurposing dr(epmc).pdf DeepDyve Pubget Overpricing