# Structure-Regularized Interpretable TCR-Epitope Prediction

arXiv: https://arxiv.org/abs/2606.30902 · HTML: https://mlsanity.com/paper/2606.30902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 47 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding](https://mlsanity.com/paper/2507.03197/) (2025)
- [Rational Multi-Modal Transformers for TCR-pMHC Prediction](https://mlsanity.com/paper/2509.17305/) (2025)

## Built on by (most cited first, 1 of 1)

- [Explainability from Training with Applications to TCR-Epitope Prediction](https://mlsanity.com/paper/2609.36354/) (2026)

## Abstract (arXiv)

T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies. Existing sequence- and structure-based models often generalize poorly to unseen epitopes and provide limited interpretability. Furthermore, the impact of generated structures on model learning remains unclear. We present TCR-SRIM, a structure-regularized interpretable-by-design model that combines protein language model embeddings with interpretable contact prototypes to capture residue-level TCR-epitope interactions. TCR-SRIM achieves state-of-the-art predictive performance and improved interpretation quality on the TCR-XAI benchmark. Using its inherent interpretability, we further evaluate the effect of generated structures on model learning. While structures predicted by AlphaFold3, TCRModel2, and tFold-TCR yield competitive performance, they lead to less accurate interaction patterns and reduced binding-site diversity than experimentally-resolved structures. Our results highlight limitations of current structure prediction models for TCR-epitope learning and demonstrate the value of interpretable-by-design models for studying generated biological structures.