Drug-target Affinity Prediction Based on Graph Transformer and Selfattention Mechanism Kinase-specific Drug-target Affinity Prediction withGraph Transformer and Self-Attention Fusion

Introduction/Objective: Traditional drug discovery methods face efficiency bottlenecks in predicting drug-target binding affinity (DTA), particularly for kinase inhibitor screening. This study proposes GTDDTA-a novel deep learning framework based on graph transformers and self-attention mechanisms-to address feature integration deficiencies and stereochemical representation limitations in kinase-targeted DTA prediction. Methods: Drug molecules were converted into graph structures using RDKit (atoms as nodes, bonds as edges). Proteins were modeled through a dual-path approach: when crystal structures were available, residue contact maps were constructed from heavy-atom coordinates extracted via Biopython with a 5.0 Å cutoff; otherwise, binarized Pconsc4-predicted contact maps were employed. Feature extraction utilized dual graph transformer layers to capture global topological dependencies in drug and target graphs, while a self-attention decoder dynamically weighted critical interaction features. The model underwent rigorous five-fold cross-validation on kinasespecific datasets (Davis and KIBA) using protein-family and molecular-scaffold partitioning strategies, with evaluation metrics including MSE, CI, Pearson correlation, and r²m. Results: GTDDTA achieved breakthrough kinase-specific performance: on the Davis dataset, MSE=0.224 (CI=0.896, Pearson=0.852) and on KIBA, MSE=0.146 (CI=0.897, Pearson=0.887). Generalization validation revealed key findings: cross-protein validation (20% kinase holdout) yielded MSE=0.3863, approaching Landrum’s experimental noise threshold, while crossscaffold validation (20% Murcko cluster holdout) showed elevated MSE=0.7455, highlighting chemical space generalization limits. Without data augmentation, the model outperformed mainstream baselines, surpassing ColdDTA by 1.7% and reducing DGraphDTA’s error by 24.8%. Discussion: GTDDTA successfully modeled conserved kinase features (e.g., VAIK homology motifs in ATP-binding pockets) through graph transformers, achieving prediction accuracy near experimental variation limits. However, 2D graph descriptors failed to encode stereochemical information (affecting 32% of chiral ligands in Davis), significantly increasing prediction errors for novel scaffolds. This limitation aligns with the fundamental challenge in kinase DTA prediction: balancing global topology modeling with 3D conformational constraints. The study further confirmed that self-attention mechanisms outperform traditional concatenation or crossattention in feature fusion quality. Conclusion: This research establishes a new state-of-the-art paradigm for kinase-specific DTA prediction: GTDDTA enables robust generalization across homologous targets through architectural innovations (graph transformers and self-attention fusion), outperforming data augmentation- dependent advanced methods. Future integration of 3D geometric learning will overcome stereochemical representation barriers, extending the model’s utility to non-kinase targets.