Cross-Level Graph Fusion With Knowledge Distillation for Efficient and Accurate Drug Repositioning

Drug repositioning accelerates the identification of novel therapeutic indications for approved compounds by leveraging established safety and pharmacokinetic profiles, thereby substantially reducing development costs and timelines. Heterogeneous graph neural networks have shown strong potential for modeling multi-relational biomedical associations among drugs, diseases, and protein targets. However, their considerable computational complexity limits scalability in high- throughput screening scenarios. Here, we present GraphKD-DR, a cross-level graph fusion framework that reconciles representational expressiveness with computational efficiency through knowledge distillation. Specifically, the teacher model integrates homogeneous similarity graphs with multi-view heterogeneous biomedical representations via Transformer-based cross-level fusion. Predictive, feature-level, and relational knowledge is then transferred from the teacher to a lightweight student model, substantially reducing model complexity while preserving predictive performance. GraphKD-DR achieves approximately 98% fewer parameters and reduces training time by 98.73-99.33%, while retaining comparable predictive performance relative to the teacher model. Experiments on three benchmark datasets demonstrate that GraphKD-DR achieves strong overall performance compared with six state-of-the-art methods across multiple evaluation metrics, including AUROC, AUPRC, and Matthews correlation coefficient. Case studies on Alzheimer's disease and breast neoplasms identify highly ranked candidate drugs with varying degrees of literature support, while molecular docking and ADMET analyses provide complementary in silico evidence for their computational prioritization. These results establish GraphKD-DR as an efficient framework for large-scale drug-disease association prediction and computational candidate prioritization.