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

arXiv: https://arxiv.org/abs/2005.10831 · HTML: https://mlsanity.com/paper/2005.10831/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 9 of 9)

- [Deep learning for drug repurposing: methods, databases, and applications](https://mlsanity.com/paper/2202.05145/) (2022)
- [Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices](https://mlsanity.com/paper/2306.00899/) (2023)
- [LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs](https://mlsanity.com/paper/2402.11804/) (2024)
- [KG-Hub -- Building and Exchanging Biological Knowledge Graphs](https://mlsanity.com/paper/2302.10800/) (2023)
- [Information Flow in Graph Neural Networks: A Clinical Triage Use Case](https://mlsanity.com/paper/2309.06081/) (2023)
- [Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review](https://mlsanity.com/paper/2309.06388/) (2023)
- [A review of feature selection strategies utilizing graph data structures and knowledge graphs](https://mlsanity.com/paper/2406.14864/) (2024)
- [MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound Protein Interactions](https://mlsanity.com/paper/2306.08907/) (2023)
- [Natural Language Processing for Drug Discovery Knowledge Graphs: promises and pitfalls](https://mlsanity.com/paper/2310.15572/) (2023)

## Abstract (arXiv)

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.