# Rational Multi-Modal Transformers for TCR-pMHC Prediction

arXiv: https://arxiv.org/abs/2509.17305 · HTML: https://mlsanity.com/paper/2509.17305/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2025.

## Built on by (most cited first, 4 of 4)

- [TCR-EML: Explainable Model Layers for TCR-pMHC Prediction](https://mlsanity.com/paper/2510.04377/) (2025)
- [Structure-Regularized Interpretable TCR-Epitope Prediction](https://mlsanity.com/paper/2606.30902/) (2026)
- [Why Does It Look There? Structured Explanations for Image Classification](https://mlsanity.com/paper/2603.10234/) (2026)
- [Explainability from Training with Applications to TCR-Epitope Prediction](https://mlsanity.com/paper/2609.36354/) (2026)

## Abstract (arXiv)

T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is fundamental to adaptive immunity and central to the development of T cell-based immunotherapies. While transformer-based models have shown promise in predicting TCR-pMHC interactions, most lack a systematic and explainable approach to architecture design. We present an approach that uses a new post-hoc explainability method to inform the construction of a novel encoder-decoder transformer model. By identifying the most informative combinations of TCR and epitope sequence inputs, we optimize cross-attention strategies, incorporate auxiliary training objectives, and introduce a novel early-stopping criterion based on explanation quality. Our framework achieves state-of-the-art predictive performance while simultaneously improving explainability, robustness, and generalization. This work establishes a principled, explanation-driven strategy for modeling TCR-pMHC binding and offers mechanistic insights into sequence-level binding behavior through the lens of deep learning.