The Role of Explainable AI in Knowledge Graph-Based Drug Repurposing: Bridging Trust and Discovery
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- 2025 IEEE International Conference on E-Business Engineering (ICEBE), 2025, 00, pp. 1-7
- Issue Date:
- 2025-11-12
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Drug repurposing presents a cost-effective and accelerated alternative to traditional drug development. With Knowledge Graphs (KGs) structuring biomedical relationships and AI models performing predictive link analysis, Explainable AI (XAI) emerges as a critical enabler for interpretability, transparency, and trust. This paper explores the central role of XAI in enhancing the reliability, ethical transparency, and regulatory compliance of AI-powered, KG-based drug repurposing. One of XAI's most important contributions is its ability to explain why a drug is predicted to treat a particular disease - by revealing key features, paths, or biological relationships that support the prediction. This allows researchers and clinicians to verify the biological plausibility of AI-driven suggestions, which is crucial for preclinical validation and clinical decision-making. The paper also discusses key XAI techniques - including feature attribution, counterfactual analysis, and path reasoning - and the challenges and ethical implications tied to biomedical use cases.
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