Active Learning for Budget-Constrained TCR--pMHC Wet-Lab Validation

Wet-lab validation of TCR-pMHC binding hypotheses is the rate-limiting step in T-cell therapy discovery: a single binding assay round can cost thousands of dollars and weeks of turnaround time, yet computational models generate thousands of candidate pairs per run. We frame this as a pool-based active learning problem: given a fixed annotation budget B, which unlabeled pairs should be sent to the assay to maximally improve a predictive model that will guide the next screening round? We introduce UDAL (Uncertainty-Diversity Active Learning), a batch acquisition strategy that combines BALD-based uncertainty estimation via MC Dropout with greedy core-set diversity selection in the encoder feature space. Evaluated on a curated VDJdb-IEDB benchmark under epitope-held-out and distance-aware protocols, UDAL achieves AUPRC 0.487 with only 5,000 queried labels--matching the performance of a model trained on 3 x more randomly sampled labels. At a budget of 2,000 labels, UDAL improves AUPRC by 16.7% over random acquisition, translating directly to fewer wasted assay slots. These results demonstrate that principled active query strategies can substantially reduce the wet-lab cost of building reliable TCR specificity models.