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arXiv AI By Jian-Qing Zheng, Hantao Lou, Zinan Yin, Sam Farrar, Yuze Zhou, Elie Antoun, Xiangxi Wang, Xuetao Cao, Tao Dong

CaliPPer: quantifying, predicting and improving AI model performance for binding prediction

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CaliPPer is a post‑hoc framework that calibrates and predicts the performance of binding‑prediction models by combining a multi‑chain Sample‑to‑Domain Distance (S2DD) metric with distance‑aware Bayesian recalibration. It operates at three resolutions—generalisability score, aggregate performance prediction, and per‑sample confidence—achieving strong distance‑performance correlations (|r| = 0.80–0.92) and low prediction errors for AUROC, AP, and F1. In retrospective analyses of five published studies, CaliPPer increased true discovery rates, improving AUROC by up to +0.20 on unseen epitopes and variants and raising confirmed neoantigen findings from 0/5 to 3/5.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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