Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases

• Published in: BMC genomics Vol. 25; no. 1; p. 584
• Main Authors: Li, Wenjun, Ma, Wanjun, Yang, Mengyun, Tang, Xiwei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.06.2024; BioMed Central; BMC
• Subjects: Algorithms; Artificial neural networks; Associations; Computational Biology - methods; Disease; Diseases; Drug Repositioning - methods; Drug repurposing; Drug-Target-Disease ternary relationship; Drugs; DTD-GNN model; Graph neural networks; Graphs; Humans; Medical innovations; Medical research; Medicine, Experimental; Methods; Modelling; Neural networks; Neural Networks, Computer; Pharmaceutical research; Regulatory approval; Social networks; Technology application; China; Drug-Target-Disease ternary relationship; Drug repurposing; DTD-GNN model
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-024-10499-5

The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure. The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.