Abstract
Advances in artificial intelligence (AI) and intelligent computing have significantly accelerated drug discovery by enabling accurate modeling of complex biomedical relationships. Among these efforts, drug repositioning-identifying novel therapeutic uses for approved or investigational drugs-offers a cost-effective and time-efficient alternative to de novo drug development. While non-negative matrix factorization (NMF) has been widely adopted for uncovering latent drug-disease associations, conventional implementations often neglect the biological context that underpins these relationships. In this work, we propose a novel NMF-based drug repositioning model that incorporates biological context (NMFIBC), which integrates drug and disease similarity networks through graph-regularized optimization to enhance predictive performance. This design enhances both the robustness and interpretability of association prediction. Extensive benchmarking on multiple gold-standard datasets demonstrates that NMFIBC outperforms existing methods across a range of metrics, including AUC, precision, and F1-score. Moreover, case studies involving clinically relevant drugs validate the biological plausibility of the predicted associations using public databases such as DrugBank, CTD, and KEGG. The proposed framework provides a powerful, context-aware AI strategy for discovering actionable insights in drug repositioning research.