A leakage-aware benchmark for evaluating chemical and cellular generalization in single-cell perturbation-response prediction
This paper introduces a leakage-aware benchmark framework that explicitly defines train-test neighborhoods to rigorously evaluate single-cell perturbation models' generalization capabilities across chemical and cellular contexts, revealing that standard random splits often overestimate performance by retaining structural and mechanistic overlaps that joint held-out validation successfully eliminates.