I-SAFE is a post-hoc auditing framework that applies quantile-based and Wasserstein coherence metrics to evaluate distributional response of DTI prediction models under structural perturbations from external priors like KLIFS annotations.
Resolving data bias improves generalization in binding affinity prediction.Nature Machine Intelligence, 7(10):1713–1725
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I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models
I-SAFE is a post-hoc auditing framework that applies quantile-based and Wasserstein coherence metrics to evaluate distributional response of DTI prediction models under structural perturbations from external priors like KLIFS annotations.