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  • Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning #MMPMID32654489
  • Zeng X; Song X; Ma T; Pan X; Zhou Y; Hou Y; Zhang Z; Li K; Karypis G; Cheng F
  • J Proteome Res 2020[Nov]; 19 (11): 4624-4636 PMID32654489show ga
  • There have been more than 2.2 million confirmed cases and over 120000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.
  • |*Betacoronavirus[MESH]
  • |*Coronavirus Infections/drug therapy/virology[MESH]
  • |*Deep Learning[MESH]
  • |*Pandemics[MESH]
  • |*Pneumonia, Viral/drug therapy/virology[MESH]
  • |Antiviral Agents[MESH]
  • |COVID-19[MESH]
  • |Drug Repositioning/*methods[MESH]
  • |Humans[MESH]
  • |Proteome[MESH]
  • |SARS-CoV-2[MESH]
  • |Transcriptome[MESH]

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  • suck abstract from ncbi

    4624 11.19 2020