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

• URL: http://arxiv.org/abs/2005.10831v1
• Date: Thu, 21 May 2020 16:02:29 GMT
• Title: Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
• Authors: Xiangxiang Zeng, Xiang Song, Tengfei Ma, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang, George Karypis, and Feixiong Cheng
• Abstract summary: There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic. There are currently no proven effective medications against COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19.
• Score: 22.01390057543923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way 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, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While 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 of accelerating therapeutic development for COVID-19.

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