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Toward better drug discovery with knowledge graph.

Xiangxiang Zeng1, Xinqi Tu1, Yuansheng Liu1

  • 1College of Information Science and Engineering, Hunan University, Changsha, 410086, China.

Current Opinion in Structural Biology
|October 14, 2021
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Summary
This summary is machine-generated.

Knowledge graphs enhance drug discovery by integrating diverse data. This review explores their use in drug repurposing and adverse reaction prediction, highlighting knowledge representation learning methods.

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Area of Science:

  • Biomedical Informatics
  • Drug Discovery
  • Data Science

Background:

  • Drug discovery relies on vast chemical and biological data.
  • Integrating heterogeneous data is a key challenge.
  • Knowledge graphs offer a structured approach to biomedical data integration.

Purpose of the Study:

  • To review knowledge graph applications in drug discovery.
  • To focus on drug repurposing and adverse drug reaction prediction.
  • To introduce knowledge representation learning for knowledge graph analysis.

Main Methods:

  • Literature review of knowledge graph-based studies.
  • Categorization of methods for drug repurposing and adverse drug reaction prediction.
  • Overview of representative knowledge representation learning models.

Main Results:

  • Knowledge graphs effectively structure and integrate diverse biomedical data.
  • Graph-based approaches show promise in drug repurposing and ADR prediction.
  • Knowledge representation learning is crucial for extracting insights from knowledge graphs.

Conclusions:

  • Knowledge graphs are valuable tools for advancing drug discovery.
  • Further research in knowledge representation learning can optimize predictive models.
  • Integrating knowledge graphs facilitates efficient identification of new drug candidates and safety profiles.