Researchers introduced a computational knowledge-graph approach to identify drug repurposing candidates and pathways for aplastic anaemia. The system combines literature mining with curated biological databases to surface molecular mechanisms that may be actionable with existing therapeutics. The work positions knowledge graphs as a practical way to prioritize hypotheses in rare diseases where clinical trial options and biomarker-driven target validation can lag behind more common indications. If validated experimentally, the output can shorten the path from mechanistic suspicion to candidate selection—supporting faster preclinical testing and potential clinical repurposing strategies.