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.
Bibliographical noteFunding Information:
We acknowledge support from the Amazon Cloud, for credits to AWS ML Services. The therapeutics discussed in this study are computationally predicted and are not currently approved for the treatment of COVID-19. The content of this publication does not necessarily reflect the views of the Cleveland Clinic.
© 2020 American Chemical Society. All rights reserved.
- deep learning
- drug repurposing
- knowledge graph
- representation learning
PubMed: MeSH publication types
- Journal Article