A Biologically Informed Vision‐Guided Framework for Interpretable T Cell Receptor–Epitope Binding Prediction

Abstract Accurate identification of the interactions between T‐cell receptors (TCRs) and antigenic epitopes presented by major histocompatibility complex (MHC) molecules is fundamental to advancing cancer immunotherapy. Nevertheless, predictive modeling of TCR–epitope binding remains challenging, as existing models struggle to generalize to unseen epitopes while often overlooking key physicochemical properties governing immune recognition. Here, a biologically informed vision‐guided deep learning framework (DAISY) is proposed for robust and interpretable TCR–epitope binding prediction. DAISY integrates hierarchical physicochemical features via a biologically inspired Condition‐Adaptive Fusion module, jointly modeling residue‐level spatial interactions and global biochemical context. DAISY consistently outperforms state‐of‐the‐art models across four generalization scenarios, notably improving ROC‐AUC by 11% and PR‐AUC by 16% over the strongest competitor in the most challenging Unseen‐Pair setting. DAISY also offers intuitive interpretability by localizing interaction‐relevant residues via Score‐CAM visualizations. Furthermore, its computational predictions are bridged to key immunological and clinical outcomes, demonstrating utility in correlating with T‐cell clonal expansion, identifying functional TCRs, and robustly forecasting patient survival. Together, DAISY can serve as a powerful tool for broad translational immunology and introduces a scalable modeling paradigm for next‐generation immune modeling.