This study addresses two key challenges in computational pharmacology: identifying novel therapeutic uses for existing drugs and modeling drug safety-related characteristics. We propose a multi-relational biomedical knowledge graph that integrates gene, drug, and disease associations with adverse effect data, enabling joint modeling of therapeutic and safety-related properties. A Relational Graph Convolutional Network (R-GCN) is employed to learn relationally aware embeddings that capture complex biological interactions across heterogeneous entities. The framework is evaluated on two tasks: (1) drug–disease link prediction for drug repurposing and (2) prediction of drug side-effect burden based on adverse event patterns. The experimental results demonstrate that the R-GCN model outperforms baseline methods, achieving 94.63% accuracy in drug–disease link prediction, while embedding-based classifiers attain up to 97.14% F1-score in side-effect burden classification. Additionally, multi-hop relational reasoning enables the discovery of biologically plausible connections between drugs, genes, and diseases. These findings highlight the effectiveness of knowledge graph-based representation learning in jointly supporting therapeutic discovery and safety-related analysis. While side-effect burden is used as a surrogate measure rather than a direct indicator of drug quality, the proposed framework provides a scalable foundation for integrating real-world pharmacovigilance and regulatory data in future studies.
Sharmin et al. (Mon,) studied this question.