TCR-SRIM uses structure regularization and contact prototypes for interpretable TCR-epitope binding prediction, reports SOTA performance on TCR-XAI, and finds generated structures produce less accurate interaction patterns than experimental ones.
Sliding-attention transformer neural architecture for predicting t cell receptor–antigen–human leucocyte antigen binding.Nature Machine Intelligence, 6(10):1216–1230, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.BM 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Structure-Regularized Interpretable TCR-Epitope Prediction
TCR-SRIM uses structure regularization and contact prototypes for interpretable TCR-epitope binding prediction, reports SOTA performance on TCR-XAI, and finds generated structures produce less accurate interaction patterns than experimental ones.