Assay concordance sets exact ceilings on what one biological score can predict

Abstract

Computational models of biology are ranked by averaging one prediction against many experimental realizations of a phenotype that are treated as interchangeable. We show this imposes an exact, model-free ceiling fixed by how much those realizations agree with each other, and that the ceiling depends on the evaluation metric through a single support-function identity. Measuring assay concordance across four public registries—2,822 MaveDB score sets, 217 ProteinGym assays, two drug screens and 1,150 CRISPR cell lines—we find that two assays of one target agree at 0.56–0.68, and that 541 domains measured twice with different proteases fix assay reliability at 0.897, so 70–90% of every ceiling is irreducible biology rather than noise. Published predictors realize 63% of the achievable on the correlation benchmarks report and 18% on the top-1% selection their users perform. We provide the estimator, the ceilings, and the measurements the field has not made.