Bidirectional Cross-Attention and Global Semantic Aggregation for Robust Drug–Target Interaction Prediction
This paper proposes a robust Transformer-based framework for drug–target interaction prediction that integrates bidirectional cross-attention, hybrid local–global semantic aggregation, and stabilized optimization to significantly outperform existing models like MolTrans, particularly in scarce-data and imbalanced scenarios.