AI RESEARCH
TCR-EML: Explainable Model Layers for TCR-pMHC Prediction
arXiv CS.LG
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ArXi:2510.04377v2 Announce Type: replace-cross T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effective approaches are black-box transformer models that cannot provide a rationale for predictions. Post-hoc explanation methods can provide insight with respect to the input but do not explicitly model biochemical mechanisms (e.g.