Full list of potential drug repurposing candidates for rare neuro-muscular disorders

Drug repurposing is particularly challenging yet essential for rare
diseases, where limited patient populations and scarce biomedical evidence
hinder traditional discovery pipelines. This work presents a holistic
machine learning approach for drug-disease link prediction, leveraging
multiple heterogeneous sources including biomedical literature, structured
databases, and textual descriptions of diseases. Focusing on seven rare
neuro-muscular disorders, we construct a biomedical knowledge graph from
literature and open databases, to evaluate a suite of rule-based, graph
neural network, and path-encoding models. An ensemble of the
best-performing methods, further enriched with disease similarity features
derived from text-based embeddings, is used to generate candidate
treatments for each disorder. Experimental results show that established
graph neural network approaches (CompGCN), and path encoding methods
(Prime Adjacency Matrix framework), outperform other approaches in metrics
like Mean Reciprocal Rank. The ensemble of the best-performing methods
further improves those metrics, reaching MRR = 0.3145. A manual validation
of top-ranked drugs from rare disease experts illustrates a high precision
(> 50 %) for drugs that potentially treat a rare disorder or its
symptoms. The lack of vast number of publications and known drug
indications for rare neuro-muscular disorders sets serious challenges in
identifying potential therapies and symptom-relievers. The ensemble
predictor incorporates rule-based, graph neural networks and path encoding
techniques, to improve drug repurposing prediction performance on a
biomedical knowledge graph created from open data. Expert evaluation
indicates that an ensemble of various knowledge graph link prediction
methods can produce promising repurposing hypotheses, for disorders
lacking any approved therapies.