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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph-Based Approach.

Pei Lou1, An Fang1, Wanqing Zhao1

  • 1Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Journal of Medical Internet Research
|October 20, 2023
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This study used a knowledge graph to identify potential drug targets and repurposed drugs for coronaviruses, offering a new approach for future viral disease research.

Keywords:
COVID-19coronavirusdrug repurposingheterogeneous data integrationinterpretable predictionknowledge graph embedding

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Virology

Background:

  • Global pandemics caused by coronaviruses (SARS, MERS, COVID-19) highlight the urgent need for effective countermeasures.
  • Coronaviruses are continuously evolving, posing an ongoing threat of future outbreaks.
  • Knowledge graphs offer a promising avenue for understanding virus pathogenicity and transmission mechanisms.

Purpose of the Study:

  • To discover potential therapeutic targets for coronaviruses.
  • To identify candidate drugs for repurposing against coronaviruses.
  • To leverage a knowledge graph-based approach for accelerated drug discovery.

Main Methods:

  • Constructed a comprehensive coronavirus knowledge graph (CovKG) by integrating biomedical literature and existing knowledge bases.
  • Developed a semantic conversion model to organize extracted literature data into semantic triples.
  • Employed knowledge graph embedding and semantic reasoning to uncover novel drug mechanisms and identify potential targets and drug candidates.

Main Results:

  • The CovKG contains over 17 million triples, integrating diverse biomedical information.
  • Semantic reasoning identified 41 novel drug mechanisms of action.
  • The study identified 33 potential targets and 18 drug candidates, including 7 novel drugs and 3 key targets (ACE2, TMPRSS2, M protein).

Conclusions:

  • Demonstrated the efficacy of a knowledge graph approach for coronavirus target discovery and drug repurposing.
  • The methodology provides a framework for biomedical knowledge discovery applicable to other viruses and diseases.