CaliPPer introduces a distance-based framework that quantifies generalizability, predicts performance metrics like AUROC with low error, and improves predictions on unseen binding data across multiple models and domains.
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CaliPPer: quantifying, predicting and improving AI model performance for binding prediction
CaliPPer introduces a distance-based framework that quantifies generalizability, predicts performance metrics like AUROC with low error, and improves predictions on unseen binding data across multiple models and domains.