Drug repurposing provides a cost-effective and time-efficient strategy to accelerate therapeutic discovery, yet most computational approaches fail to capture the multi-scale biomedical mechanisms underlying drug-disease associations, limiting interpretability. We introduce BioMNEDR (mechanism-guided network embedding for drug repurposing) that integrates heterogeneous biomedical networks through biologically curated meta-paths. BioMNEDR generates low-dimensional embeddings preserving protein-protein interactions and functional hierarchies. It further integrates multi-path predictions through an XGBoost classifier. The framework achieves state-of-the-art performance, consistently surpassing strong baselines across AUROC, AUPR, recall, and F1-score, while maintaining a balanced trade-off in precision. Case studies further highlight its practical utility, demonstrating the ability to rediscover approved drugs and prioritize promising candidates, such as cromoglicic acid for Alzheimer's disease. By explicitly modeling multi-scale mechanisms, BioMNEDR enhances both predictive accuracy and biomedical interpretability, offering a robust computational framework for systematic drug repurposing.
Zeng et al. (2026) studied this question.