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

TLDR

The COVID‑19 pandemic has caused over 2.2 million cases and 120 000 deaths in the U.S., yet no proven effective medications exist, making drug repurposing a promising strategy. This study develops an integrative, network‑based deep‑learning framework (CoV‑KGE) to identify repurposable drugs for COVID‑19. By constructing a 15‑million‑edge knowledge graph linking drugs, diseases, proteins, pathways, and expression from 24 million PubMed articles, and applying a network‑based deep‑learning model on AWS, the authors screened for candidate therapeutics. The approach identified 41 repurposable drugs—including dexamethasone, indomethacin, niclosamide, and toremifene—validated by transcriptomic, proteomic, and clinical‑trial data, demonstrating a powerful method to accelerate COVID‑19 therapeutic development.

Abstract

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.