Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning

Xiangxiang Zeng, Xiang Song, Tengfei Ma, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang, Kenli Li, George Karypis, Feixiong Cheng

Research output: Contribution to journalArticlepeer-review

153 Scopus citations


There have been more than 2.2 million confirmed cases and over 120 »000 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.

Original languageEnglish (US)
Pages (from-to)4624-4636
Number of pages13
JournalJournal of Proteome Research
Issue number11
StatePublished - Nov 6 2020

Bibliographical note

Publisher Copyright:
© 2020 American Chemical Society. All rights reserved.


  • COVID-19
  • SARS-CoV-2
  • deep learning
  • drug repurposing
  • knowledge graph
  • representation learning


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