Accurate prediction of microbe-drug associations is essential for drug development and disease diagnosis. However, existing methods often struggle to capture complex nonlinear relationships, effectively model long-range dependencies, and distinguish subtle similarities between microbes and drugs. To address these challenges, this paper introduces a new model for microbe-drug association prediction, CLMT. The proposed model differs from previous approaches in three key ways. Firstly, unlike conventional GCN-based models, CLMT leverages a Graph Transformer network with an attention mechanism to model high-order dependencies in the microbe-drug interaction graph, enhancing its ability to capture long-range associations. Then, we introduce graph contrastive learning, generating multiple augmented views through node perturbation and edge dropout. By optimizing a contrastive loss, CLMT distinguishes subtle structural variations, making the learned embeddings more robust and generalizable. By integrating multi-view contrastive learning and Transformer-based encoding, CLMT effectively mitigates data sparsity issues, significantly outperforming existing methods. Experimental results on three publicly available datasets demonstrate that CLMT achieves state-of-the-art performance, particularly in handling sparse data and nonlinear microbe-drug interactions, confirming its effectiveness for real-world biomedical applications. On the MDAD, aBiofilm, and Drug Virus datasets, CLMT outperforms the previously best model in terms of Accuracy by 4.3%, 3.5%, and 2.8%, respectively.
