关键词: 药物-靶标相互作用预测, 结构-网络协同特征, KAN, 图神经网络, 注意力机制

Abstract: Drug-Target Interaction (DTI) prediction is a key task in drug discovery and repurposing. The challenge lies in how to integrate multi-source heterogeneous features to comprehensively characterize the complex relationships between drugs and targets. To address the shortcomings of traditional methods that rely on a single data source and model complex nonlinear relationships, a DTI prediction method based on Structure-Network collaborative features and grid-attention enhanced Kolmogorov-Arnold network (SNKDTI) was proposed. First, a feature extraction strategy based on structure and network collaboration was designed: for drug representation, molecular fingerprints were fused with graph embedding methods to quantify chemical structures; for target representation, traditional physicochemical encoding was combined with pre-trained models to extract sequence features. Meanwhile, heterogeneous networks such as drug-disease associations and protein-protein interactions were introduced, and network topological features were extracted by using the Random Walk with Restart (RWR) algorithm. And these features were compressed by using a Denoising AutoEncoder (DAE) to integrate structural and network information of drugs and targets. Second, a Heterogeneous Biological Information Network (HBIN) was constructed, with feature propagation carried out using a Graph Convolutional Network (GCN). Additionally, a Grid-Attention enhanced KAN (GA-KAN) was proposed, which introduced multiple learnable B-spline basis function grids and an attention mechanism to achieve adaptive combinations of multiple nonlinear mapping modules, thereby enhancing the model’s expressive power and input adaptability. Finally, a Gradient Boosting Decision Tree (GBDT) was used to build an end-to-end prediction framework. The proposed method and benchmark methods are compared on public datasets. The results show that SNKDTI achieves relative improvements of 0.81%, 1.36%, and 3.29% in Area Under the receiver operating characteristic Curve (AUC), Area Under the Precision-Recall curve (AUPR) and F1-score, respectively, over the best-performing benchmark methods for each corresponding metric. Therefore, the SNKDTI significantly enhances accuracy, robustness, and generalization ability, providing an efficient tool for new drug target screening.

Key words: Drug-Target Interaction (DTI) prediction, structure-network collaborative feature, KAN (Kolmogorov-Arnold Network), Graph Neural Network (GNN), attention mechanism