Iterative attack-and-defend framework for improving TCR-epitope binding prediction models.

ABSTRACT:

Motivation

Reliable TCR-epitope binding prediction models are essential for development of adoptive T cell therapy and vaccine design. These models often struggle with false positives, which can be attributed to the limited data coverage in existing negative sample datasets. Common strategies for generating negative samples, such as pairing with background T cell receptors (TCRs) or shuffling within pairs, fail to account for model-specific vulnerabilities or biologically implausible sequences.

Results

To address these challenges, we propose an iterative attack-and-defend framework that systematically identifies and mitigates weaknesses in TCR-epitope prediction models. During the attack phase, a reinforcement learning from AI feedback (RLAIF) framework is used to attack a p

SUBMITTER: Zhang P 

PROVIDER: S-EPMC12261473 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature