Cross-scale semantic fusion integration of dual pathway models in drug repositioning.

Abstract

Drug Repositioning (DR) represents an innovative drug development strategy that significantly reduces both cost and time by identifying new therapeutic indications for approved drugs. Current methods primarily focus on extracting information from drug-disease networks, but often overlook critical local structural details between nodes. This study introduces CSDPDR, a novel Dual-branch graph neural network that integrates Topology Feature Information and Salient Feature Information to enhance drug repositioning accuracy and efficiency. Through the Topology-aware branch with Adaptive Residual Graph Attention and the Saliency-aware branch with Score-Driven Top-K Convolutional Graph Pooling, the model can capture both large-scale topology patterns and fine-grained local information. Furthermore, our approach effectively alleviate graph sparsity issues through meta-path-based network enhancement and confidence-based filtering mechanisms. Comparative experiments on two benchmark datasets an additional dataset demonstrate that CSDPDR significantly outperforms several state-of-the-art baseline methods. Case studies on Alzheimer's disease and breast neoplasms further validate the model's practical applicability and effectiveness.