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

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.