Drug repurposing, also known as drug repositioning, offers a promising strategy for identifying new therapeutic uses for existing drugs. In the context of rare diseases, where traditional drug discovery approaches face significant challenges, bioinformatics plays a critical role in facilitating the repurposing of drugs to address unmet medical needs. This review examines bioinformatics approaches for drug repurposing in rare diseases, highlighting the methodologies, data sources, and computational tools used to identify potential candidates. By leveraging omics data, network analysis, machine learning, and computational modeling, bioinformatics enables the systematic exploration of drug-disease associations and the prioritization of repurposing candidates based on biological relevance and therapeutic potential. Despite the complexity and heterogeneity of rare diseases, bioinformatics-driven drug repurposing holds promise for accelerating the discovery and development of treatments for these underserved patient populations.
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