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

## TL;DR

DAISY is a new deep learning framework that improves prediction of T cell receptor binding to antigens, with better accuracy and interpretability for cancer immunotherapy.

## Contribution

DAISY introduces a biologically informed vision-guided framework that outperforms existing models and provides interpretable insights into TCR–epitope interactions.

## Key findings

- DAISY outperforms state-of-the-art models in TCR–epitope binding prediction, with 11% higher ROC-AUC and 16% higher PR-AUC in the Unseen-Pair setting.
- DAISY provides interpretable results through Score-CAM visualizations, identifying interaction-relevant residues.
- DAISY correlates with immunological outcomes like T-cell clonal expansion and patient survival in immunotherapy.

## 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.

Integrating hierarchical physicochemical features via a bio‐inspired design, the DAISY framework robustly predicts T‐cell receptor binding, significantly outperforming state‐of‐the‐art models on unseen epitopes. It uniquely offers visual interpretability via Score‐CAM visualizations and profound clinical relevance, correlating with T‐cell expansion, enabling functional TCR screening, and defining functional biomarkers that forecast patient survival in immunotherapy.