Drug repositioning aims to identify new indications for existing drugs, offering a cost-effective and time-efficient strategy for therapeutic development. Its core challenge lies in accurately predicting potential drug-disease associations (DDAs). However, existing computational approaches often suffer from inadequate drug representation, insufficient modeling of disease semantics, and imbalanced data distributions, which collectively limit predictive accuracy and generalization ability. To address these challenges, we propose an innovative framework, termed XRepDDA, that integrates multimodal feature representation with deep metric learning to improve DDA prediction accuracy and robustness. For drug representation, the SMI-TED pretrained chemical language model encodes SMILES sequences into chemically informative molecular embeddings. For disease representation, a hierarchical semantic graph based on the MeSH ontology is constructed together with a semantic-enhanced graph embedding strategy to capture hierarchical and semantic relationships among diseases. To mitigate class imbalance, we applied the AllKNN adaptive undersampling strategy. The prediction module is built upon an improved ModernNCA architecture, which learns a discriminative embedding space through deep metric learning. Experiments on multiple public benchmark data sets demonstrate that XRepDDA consistently outperforms diverse baseline models, including traditional machine learning, tree-based ensemble, and deep learning methods, achieving AUC and AUPR values of up to 0.9990 and 0.9991, respectively. Furthermore, molecular docking experiments on top-ranked candidate drugs for Alzheimer's disease and stomach neoplasms provide in silico validation of predictive reliability. To enhance interpretability, a multilevel explainability framework is established, combining SHAP-based global feature attribution with attention mechanisms and molecular perturbation analyses to identify key features and pharmacophores at the local level. These results support the chemical interpretability and the biological plausibility of the predictions.
Zhang et al. (Fri,) studied this question.