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Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug.

Hanieh Abbasi1, Amir Lakizadeh1

  • 1Computer Engineering Department, University of Qom, Qom, Iran.

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Summary

This study introduces a novel hypergraph neural network for drug repurposing, efficiently predicting drug-disease associations. The method models complex relationships using hypergraphs, improving accuracy and reducing data requirements for identifying new therapeutic uses of existing drugs.

Keywords:
drug–disease association predictiongraph neural networkhypergraphnetwork embedding

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Area of Science:

  • Computational biology
  • Pharmacology
  • Artificial intelligence in medicine

Background:

  • Drug development is lengthy and costly, driving interest in drug repositioning.
  • Computational methods, including graph and hypergraph approaches, are vital for identifying drug-disease associations.
  • Existing methods may not fully capture complex interrelationships.

Purpose of the Study:

  • To present a novel drug repurposing method using a hypergraph neural network.
  • To predict drug-disease associations more effectively and efficiently.
  • To overcome limitations of traditional graph-based approaches.

Main Methods:

  • Constructing a heterogeneous graph of drugs and diseases.
  • Converting the graph to a hypergraph by grouping diseases under shared drugs into hyperedges.
  • Applying a graph neural network to predict drug-disease associations based on the hypergraph structure.

Main Results:

  • The hypergraph model effectively captures complex drug-disease interrelationships.
  • The method demonstrates improved accuracy in predicting drug-disease associations compared to state-of-the-art techniques.
  • The approach requires less extensive data by utilizing a drug-disease association matrix.

Conclusions:

  • The hypergraph neural network offers a more efficient and accurate approach to drug repurposing.
  • This method enhances the identification of new therapeutic applications for existing drugs.
  • The model's ability to model complex relationships makes it a valuable tool in computational drug discovery.