GCL-MSE: Graph Contrastive Learning with Mutual Similarity Enhancement for Drug Repositioning

Amidst the shift to data-driven drug repositioning, existing models struggle to capture complex semantic and topological relationships in biomedical knowledge graphs for drug-disease association (DDA) mining. We propose a Graph Contrastive Learning with Mutual Similarity Enhancement (GCL-MSE). The core innovation lies in defining a concept of mutual similarity. This concept comprises two aspects: drug therapeutic domain similarity, which captures the functional associations between drugs based on their therapeutic spectra, and disease pharmacological response similarity, which reflects the pathological associations between diseases based on drug response patterns. Based on this concept, a Mutual Similarity Enhancement mechanism (MSE) is constructed to fuse four similarities, to build a semantic relationship topology that captures the complex semantic dependencies in DDA. Further, an Adaptive Orthogonal Noise Contrastive Estimation Loss (AdaOrthoNCE) is proposed to disentangle biological relationships in the latent space while optimizing discriminative representations. GCL MSE integrates the semantic topology via MSE, employs a three-channel graph convolutional model to generate topology-semantic co-representations, and utilizes AdaOrthoNCE to learn optimized embeddings, ultimately enabling cross-scale DDA prediction. Experimental results demonstrate that GCL-MSE significantly outperforms state of-the-art models in both AUROC and AUPRC metrics, with improvements of over 4.8% in AUROC and 25.5% in AUPRC, thereby validating the effectiveness of collaborative modeling that integrates features from pharmacological, therapeutic, and topological perspectives. Additionally, GCL MSE predicts the therapeutic roles of drugs such as lamotrigine for Alzheimer's disease and hydroxyurea for breast cancer. Molecular docking experiments and related studies further confirm its validity.