A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It
Standard conformal prediction fails to maintain coverage on imbalanced datasets, leaving minority classes dangerously exposed in drug discovery tasks.
Researchers found that while marginal conformal prediction meets global error rate targets, it significantly under-covers minority classes. In tests like clinical-trial toxicity, coverage dropped to 4.2%. The team proposes a class-conditional calibration method to restore reliable coverage for minority samples.
