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Related Experiment Video

Updated: Jun 5, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction.

Rongting Yue1, Abhishek Dutta2

  • 1Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, USA. rongting.yue@uconn.edu.

Interdisciplinary Sciences, Computational Life Sciences
|December 4, 2024
PubMed
Summary

This study uses computational systems biology and Graph Convolutional Networks (GCNs) to rapidly identify potential drug repurposing candidates for emerging infectious diseases like Zika and COVID-19.

Keywords:
Drug repurposingGraph learningHeterogeneous graphKronecker productSensitivity analysis

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Area of Science:

  • Computational systems biology
  • Bioinformatics
  • Drug discovery

Background:

  • Emerging infectious diseases necessitate rapid therapeutic development.
  • Computational approaches are crucial for identifying drug candidates through repurposing.
  • Existing methods require enhancement for speed and accuracy.

Purpose of the Study:

  • To develop a computational pipeline for rapid drug repurposing against emerging infectious diseases.
  • To enhance prediction performance using Graph Convolutional Networks (GCNs) and sensitivity analysis.
  • To identify novel drug candidates for Zika virus and COVID-19.

Main Methods:

  • Developed novel analytical expressions for sensitivity analysis using the Kronecker product.
  • Implemented sensitivity-based graph reduction to refine models.
  • Integrated RNA-seq data, molecular interactions, and GCNs to construct heterogeneous graphs.
  • Applied the pipeline to Zika virus and COVID-19 datasets.

Main Results:

  • Identified disease-related genes and pathways for Zika and COVID-19.
  • Successfully predicted potential drug candidates, including Betamethasone phosphate and Bizelesin for Zika, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19.
  • Validated candidate drugs through literature review and docking analysis.
  • Demonstrated a scalable and cost-effective computational drug repurposing pipeline.

Conclusions:

  • The proposed computational approach significantly enhances drug repurposing efficiency for urgent healthcare needs.
  • Sensitivity-based graph reduction improves prediction accuracy in GCN models.
  • This methodology offers a promising strategy for combating emerging infectious diseases through rapid drug discovery.