# Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

arXiv: https://arxiv.org/abs/2009.10931 · HTML: https://mlsanity.com/paper/2009.10931/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 10 of 10)

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- [Curriculum Graph Machine Learning: A Survey](https://mlsanity.com/paper/2302.02926/) (2023)
- [KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description](https://mlsanity.com/paper/2212.01384/) (2022)
- [Out-of-Distribution Generalization on Graphs via Progressive Inference](https://mlsanity.com/paper/2503.02988/) (2025)
- [Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2306.10456/) (2023)
- [Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks](https://mlsanity.com/paper/2312.02387/) (2023)
- [Active Learning for Graphs with Noisy Structures](https://mlsanity.com/paper/2402.02321/) (2024)
- [A review of feature selection strategies utilizing graph data structures and knowledge graphs](https://mlsanity.com/paper/2406.14864/) (2024)
- [MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2501.06823/) (2025)
- [A survey of using EHR as real-world evidence for discovering and validating new drug indications](https://mlsanity.com/paper/2505.24767/) (2025)

## Abstract (arXiv)

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This is a post-peer-review, pre-copyedit version of an article published in Scientific Reports The final authenticated version is available online at: https://www.nature.com/articles/s41598-021-02353-5