Deep Learning-Based Genetic Perturbation Models Do Outperform Uninformative Baselines on Well-Calibrated Metrics

Single cell genetic perturbation modeling involves predicting the effects of unobserved genetic manipulations, enabling scalable in silico screens for target discovery. Recent reports have claimed that deep learning-based perturbation models fail to outperform uninformative baselines, raising doubts about their utility. Here, we show that these conclusions largely stem from limitations of benchmarking metrics , not from the models themselves. We introduce a framework for evaluating bench-mark metric calibration using positive and negative controls, including a new positive control baseline (the interpolated duplicate ) and a quantitative calibration measure (the dynamic range fraction ). Across 14 perturbation datasets and 13 evaluation metrics, we find that conventional metrics such as mean squared error (MSE) and control-referenced delta correlation (Pearson(Δ _ctrl )) are often poorly calibrated, whereas weighted and rank-based alternatives exhibit consistent calibration. Under well-calibrated metrics, deep learning models outperform mean, control, and linear baselines, and in some cases even surpass the additive baseline in combination-prediction tasks. Calibrated evaluation thus explains prior reports of model underperformance, revealing that deep learning models do outperform uninformative baselines.