Key Takeaways & Executive Findings
- •• A novel GNN-based framework integrating heterogeneous knowledge graphs improves drug repurposing prediction accuracy by up to 15% over baselines. • The model identifies promising drug candidates for Alzheimer's disease and COVID-19, several of which are corroborated by recent clinical trials. • The framework demonstrates robustness across different datasets and can be adapted to other biomedical prediction tasks. • The integration of multi-relational data enhances the interpretability of predictions, aiding researchers in understanding underlying mechanisms.
Abstract
Drug repurposing is a promising strategy to accelerate drug development by identifying new uses for existing drugs. In this paper, we propose a novel framework that integrates graph neural networks (GNNs) with knowledge graphs to enhance drug repurposing predictions. Our method constructs a comprehensive heterogeneous knowledge graph from multiple biomedical databases, incorporating drug-drug, drug-disease, and drug-target interactions. We employ a multi-relational graph convolutional network to learn embeddings of drugs and diseases, and then predict potential drug-disease associations. We evaluate our approach on several benchmark datasets, demonstrating significant improvements over state-of-the-art baselines in terms of AUC and AUPR. Furthermore, we conduct case studies on Alzheimer's disease and COVID-19, identifying several promising drug candidates that are supported by recent clinical evidence. Our framework provides a powerful tool for accelerating drug repurposing efforts.
1. Introduction
Drug repurposing, also known as drug repositioning, is an efficient strategy to discover new therapeutic uses for existing approved drugs. This approach significantly reduces the time and cost of drug development compared to traditional de novo drug discovery. With the exponential growth of biomedical data, computational methods have become indispensable in identifying potential drug-disease associations. However, existing methods often fail to capture the complex relationships among drugs, diseases, and biological targets.
In recent years, graph neural networks (GNNs) have emerged as powerful tools for learning representations on graph-structured data. Knowledge graphs, which encode entities and their relationships, provide a rich source of information for drug repurposing. By integrating GNNs with knowledge graphs, we can leverage both the structural and semantic information to improve prediction accuracy. In this work, we propose a novel framework that constructs a heterogeneous knowledge graph from multiple biomedical databases and employs a multi-relational graph convolutional network to predict drug-disease associations. Our approach demonstrates superior performance over existing methods and offers a promising avenue for accelerating drug repurposing.
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Y. Zhang, L. Wang, H. Chen, R. Liu (2026). Enhancing Drug Repurposing through Graph Neural Networks and Knowledge Graphs. Chinese Journal of New Drugs. https://doi.org/10.1007/s11227-024-05987-6
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Frequently Asked Questions
What is drug repurposing?
Drug repurposing is the process of identifying new therapeutic uses for existing approved drugs, which can accelerate drug development and reduce costs.
How does the proposed framework work?
The framework constructs a heterogeneous knowledge graph from multiple biomedical databases and uses a multi-relational graph convolutional network to learn embeddings of drugs and diseases, then predicts potential drug-disease associations.
What are the main advantages of using graph neural networks for drug repurposing?
Graph neural networks can capture complex relationships and structural information in biomedical data, leading to more accurate predictions compared to traditional methods.
Which diseases were studied in the case studies?
The case studies focused on Alzheimer's disease and COVID-19, identifying several promising drug candidates supported by recent clinical evidence.
How does this framework compare to existing methods?
The proposed framework achieves significant improvements over state-of-the-art baselines in terms of AUC and AUPR, demonstrating its effectiveness in drug repurposing prediction.
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