TransFusionDR: A Framework for Drug Repositioning via Contrastive and High-Order Feature Fusion with Transformers

Drug repositioning can effectively reduce research and development costs and accelerate time to market by identifying new indications for existing drugs. In recent years, deep learning-based methods have achieved remarkable progress in the field of drug repositioning. However, current approaches still suffer from several limitations. First, many methods simply concatenate structural information and association information without fully capturing their intrinsic relationships, leading to suboptimal information fusion. Second, most models lack mechanisms for extracting high-order structural information and aligning heterogeneous and homogeneous features, which limits the model's expressiveness and predictive performance. To address these limitations, we propose TransFusionDR, a framework for drug repositioning via contrastive and high-order feature fusion with transformers. We employ a Graph Transformer to extract deep structural features from drug-drug and disease-disease graphs, and utilize a Heterogeneous Graph Transformer to capture semantic information from the drug-disease association graph. These features are then refined through contrastive learning to enhance semantic consistency and improve information fusion quality. We further introduce a Transformer Encoder to deeply integrate the homogeneous and heterogeneous features by dynamically modeling semantic dependencies, enabling the extraction of high-order interactions and achieving more effective feature alignment. Experimental results on two benchmark datasets demonstrate that the proposed framework significantly improves prediction accuracy and robustness in drug repositioning tasks, outperforming state-of-the-art methods.