Rewarding explainability in drug repurposing with knowledge graphs

Drug repurposing often starts as a hypothesis: a known compound might help treat a disease beyond its original indication. Knowledge graphs are a natural place to look for such hypotheses because they encode biomedical entities (drugs, genes, phenotypes, diseases) and their relations. In KG terms, that repurposing can be framed as a triple (). However, many link prediction methods trade away interpretability for raw accuracy, making it hard for scientists to see why a suggested drug should work. We argue that for AI to function as a reliable scientific tool, it must deliver scientifically grounded explanations, not just scores.