A Novel Graph Transformer Framework for Predicting Drug-Disease Associations with Structural Awareness.

Abstract

Accurately predicting drug-disease associations (DDAs) is essential for accelerating the discovery of novel therapeutics. Graph representation learning-based computational models have become increasingly popular for this task due to their efficiency and cost-effectiveness. However, existing approaches often suffer from structural inductive biases and a limited ability to capture the rich heterogeneous context of biomedical molecules, which constrains their capacity to learn expressive drug and disease representations. To address this issue, we propose SGTL-DDA, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs). SGTL-DDA integrates a meta-path-guided sampling strategy with a multi-level attention mechanism, enabling the model to jointly learn from both structural dependencies and attribute semantics in an end-to-end manner. Extensive experiments on two benchmark datasets demonstrate that SGTL-DDA consistently outperforms state-of-the-art methods in terms of Accuracy, F1-score, and AUC under a ten-fold cross-validation scheme. Furthermore, case studies on Alzheimer's disease and breast cancer confirm the predictive capability of SGTL-DDA, as it successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.