The Ginkgo Datapoints Antibody Developability (AbDev) Competition, a blinded benchmark for developability prediction characterized entirely on a single, industrial-scale experimental platform, was conducted from September 8 to November 18, 2025. We benchmarked predictors across five biophysical properties - hydrophobicity, thermostability, self-association, expression titer, and polyreactivity - using a public training set of 246 clinical antibodies and a blinded, held-out test set of 80 antibodies. We received submissions from 113 teams spanning 25 countries, 38 companies, and 39 universities. Winning submissions differed by assay. Top Spearman's ρ values on the test set reached 0.708 (hydrophobicity), 0.392 (thermostability), 0.356 (polyreactivity), 0.337 (self-association), and 0.310 (titer). Cross-validation scores from the public training set consistently exceeded held-out test performance, indicating overfitting and limited out-of-distribution generalization. Together, these results provide a standardized snapshot of current antibody developability modeling capabilities and highlight a key bottleneck: available datasets are too small and heterogeneous to support robust, assay-spanning prediction. Meaningful progress will require larger, standardized, and diverse experimental datasets - with harmonized protocols and rich metadata - to train and validate models that generalize reliably for future antibody discovery campaigns.
Niekerk et al. (Sun,) studied this question.