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Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning
Xiangxiang Zeng1, Xiang Song2, Tengfei Ma1
1School of Computer Science and Engineering, Hunan University, Changsha 410012, China.
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.
Insights
A new deep-learning method identified 41 potential drugs for COVID-19 by analyzing scientific literature. This approach accelerates the search for effective treatments against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic.
Area of Science:
- Computational biology
- Pharmacology
- Artificial intelligence in medicine
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, has resulted in millions of cases and deaths globally.
- Effective treatments for COVID-19 are limited, necessitating novel therapeutic strategies.
- Drug repurposing presents a viable approach to accelerate the development of COVID-19 therapies.
Purpose of the Study:
- To develop and apply an integrative, network-based deep-learning methodology for identifying repurposable drugs against COVID-19.
- To create a comprehensive knowledge graph integrating diverse biological and medical data.
- To validate identified drug candidates using transcriptomic, proteomic, and clinical trial data.
Main Methods:
- Construction of a large-scale knowledge graph with 15 million edges from 24 million PubMed publications, encompassing drugs, diseases, proteins, pathways, and gene expression.
- Application of a network-based deep-learning framework on cloud computing resources (AWS) to analyze the knowledge graph.
- Validation of identified drug candidates through transcriptomic and proteomic data from SARS-CoV-2-infected cells and clinical trial information.
Main Results:
- Identification of 41 repurposable drugs with potential therapeutic associations for COVID-19.
- Key identified drugs include dexamethasone, indomethacin, niclosamide, and toremifene.
- Therapeutic associations were supported by experimental data and ongoing clinical trial evidence.
Conclusions:
- The study demonstrates a powerful deep-learning methodology (CoV-KGE) for prioritizing existing drugs for COVID-19 treatment.
- This approach has the potential to significantly accelerate the discovery and development of new therapies.
- While not recommending specific drugs, the methodology offers a valuable tool for future drug repurposing efforts in infectious diseases.
