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HGTDR: Advancing drug repurposing with heterogeneous graph transformers.

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  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9517, Iran.

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Drug repurposing accelerates drug development by using a novel Heterogeneous Graph Transformer (HGTDR). This systematic approach handles diverse data, improving cost-effectiveness and patient outcomes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Drug Discovery

Background:

  • Drug repurposing offers a cost-effective alternative to traditional drug development.
  • Existing biological network-based methods face limitations in data handling and end-to-end functionality.
  • A systematic approach is needed to overcome challenges in data heterogeneity and information loss.

Purpose of the Study:

  • To introduce Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), a novel systematic approach.
  • To address limitations of current methods in handling large and diverse biological datasets.
  • To improve the efficiency and accuracy of drug repurposing.

Main Methods:

  • Constructing a heterogeneous knowledge graph.
  • Utilizing a heterogeneous graph transformer network for data analysis.
  • Employing a fully connected network to compute relationship scores.

Main Results:

  • HGTDR demonstrates comparable performance to existing drug repurposing methods.
  • Validation through medical studies confirms promising results for top drug repurposing suggestions.
  • HGTDR successfully predicts various inter-relations, including drug-protein and disease-protein interactions.

Conclusions:

  • HGTDR provides an effective and systematic solution for knowledge graph-based drug repurposing.
  • The method overcomes challenges related to data heterogeneity and scope limitations.
  • HGTDR shows potential for advancing drug discovery and improving human health.