DVMMHGNN: A Dual View Multi-Modal Heterogeneous Graph Neural Network with Contrastive Learning for Microbe-Informed Drug Repurposing

Drug repurposing (DR) offers an efficient and cost-effective strategy for pharmaceutical development by identifying new therapeutic applications for existing drugs. The effectiveness of this approach relies on accurately uncovering potential drug-disease associations; however, capturing the complex biological interactions underlying these associations remains a major challenge. Current computational approaches frequently overlook the critical regulatory role of the microbiota in modulating drug action pathways. Moreover, many methods fail to preserve semantic consistency during multimodal biological data integration and heterogeneous graph augmentation, thereby limiting their representational capacity. To overcome these limitations, we propose DVMMHGNN, a heterogeneous graph contrastive learning framework for microbe informed drug repurposing that jointly integrates structural and meta-path information. First, a multimodal feature fusion module embeds heterogeneous biological entities into a unified latent space to ensure cross-modal feature alignment. Second, a graph-masked autoencoder is employed to capture high-order representations from similarity networks. Finally, DVMMHGNN enhances semantic coherence through contrastive learning at both the structural and meta-path levels, aligning embeddings across multiple views to effectively capture both local and global semantics. Experimental evaluations on the constructed benchmark data set demonstrate that DVMMHGNN consistently outperforms nine state-of-the-art methods in predicting drug-disease associations, achieving superior performance across AUC, AUPR, and F1-score metrics. Ablation studies further validate the contribution of each model component, while case analyses highlight the potential of DVMMHGNN to identify novel drug indications and guide therapeutic strategy development.