Drug repurposing for rare diseases via a gene-bridged heterogeneous knowledge graph and graph attention network

Rare diseases are severely underserved by pharmacological treatments, and computational drug repurposing offers a cost-effective alternative to de novo discovery. We present a reproducible end-to-end pipeline integrating 3,961 rare disease–gene associations from Orphadata with 98,239 gene–drug records from DisGeNET through a multi-stage harmonization pipeline (HGNC symbol standardization and RapidFuzz fuzzy matching), yielding a large-scale gene-bridged rare disease tripartite knowledge graph — to our knowledge the largest such graph constructed exclusively from Orphadata and DisGeNET— comprising 15,454 nodes and 35,131 edges spanning 2,249 clinically distinct rare diseases. A Graph Attention Network (GAT) trained on node-type classification as a pretext task achieves macro F1 = 0.651 and ROC-AUC = 0.818 on a stratified held-out test set, with stable performance across five evaluation partitions (SD ≤ 0.007). Drug candidate retrieval via cosine similarity in the GAT embedding space achieves Hits@10 = 0.400 across 200 evaluated disorders (vs. < 0.001 random baseline), with the clinically validated drug NITISINONE recovered at rank 4 for a tyrosine catabolism pathway disorder without pathway annotations. A deployment-ready interface is publicly available on HuggingFace Spaces.