CaliPPer: quantifying, predicting and improving AI model performance for binding prediction
Binding prediction models accelerate therapeutic antibody and TCR discovery, but their performance on new datasets is unpredictable, often leading to low discovery rates. Density-ratio methods (PAPE, M-CBPE) provide label-free performance estimation for binary classification, but their assumptions and aggregate-only outputs limit binding prediction on neoepitopes, antigen variants and chemical scaffolds. Here we present CaliPPer (Calibration and Prediction of Performance), a post-hoc framework pairing a multi-chain Sample-to-Domain Distance (S2DD) with distance-aware Bayesian recalibration, operating at three resolutions: generalisability score, aggregate performance prediction, and per-s...
| Authors | Jian-Qing Zheng, Hantao Lou, Zinan Yin et al. |
|---|---|
| Primary category | cs.CE |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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