Knowledge Graph-Based Modeling of Compound–Target–Disease Associations for Drug Repurposing
Keywords:
knowledge graphs, drug repurposing, compound-target-disease associations, systems pharmacology, biomedical informatics, graph machine learning, data governanceAbstract
Drug repurposing seeks to identify new therapeutic uses for existing compounds, yet the underlying evidence remains fragmented across chemical, biological, pharmacological, and clinical resources. Knowledge graphs provide a principled framework for integrating these heterogeneous relationships and reasoning over compound–target–disease associations in ways that isolated databases cannot support. This article presents a systems-level examination of knowledge graph-based modeling for drug repurposing, emphasizing architectural choices, governance mechanisms, inference trade-offs, and deployment constraints rather than algorithmic detail. The discussion addresses how entity alignment, ontology mapping, and provenance-aware ingestion shape the quality and trustworthiness of graph-based evidence. It further compares embedding-based and path-based inference, focusing on interpretability, scalability, robustness, and calibration under incomplete biomedical knowledge. The article also considers how graph-driven repurposing can influence research priorities and how governance structures can reduce disparities in representation across disease areas and populations. We argue that sustainable and responsible graph-based repurposing requires treating the knowledge graph as a governed socio-technical infrastructure rather than a static analytical artifact. Illustrative comparisons with network pharmacology and polypharmacy modeling are used to clarify structural trade-offs, and the discussion extends to data stewardship, model documentation, regulatory alignment, and institutional sustainability.
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