A Graph-Based Multi-Dimensional Interaction Network for Drug-Drug Interaction Prediction

In the treatment of complex diseases, drug combination therapy is common, but drug-drug interactions (DDI) can cause severe side effects, threaten patient safety, and increase healthcare costs. Existing DDI prediction methods often focus on drug substructure features but overlook the complex interactions between them. To address this, this paper proposes the Multi-dimensional Interaction Graph Neural Network (MDI-DDI). The model combines four GraphSAGE convolution layers with a Tri-Co Attention Module. It first calculates interaction strength at the 2D level using a co-attention mechanism, then captures deeper interactions at the 3D level using a triplet structure. Experimental results show that MDI-DDI outperforms existing methods, achieving ACC, AUPRC, and AUROC of 0.9613, 0.9901, and 0.9871, respectively, on the DrugBank dataset. Additionally, the risk analysis of nitrate and nitrite drugs demonstrates the model's ability to accurately identify key functional groups, further validating its interpretability.