A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation models
Single-cell drug perturbation models are increasingly used to predict how compounds remodel cellular states, but they are still largely assessed by expression reconstruction. Whether high expression similarity reflects preservation of drug-response signatures remains unclear. Here we present scDrugPerturb-Bench, a mechanism-annotated benchmark that links matched control and drug-treated single-cell RNA-sequencing profiles to literature-curated directional key-gene evidence. The resource covers 181 datasets, 423 annotated response cases, 717 unique key genes and 2.5 million cells. We introduce the Mechanism Fidelity Score (MFS) to evaluate key-gene direction, effect-size recovery, gene-set co-herence, mechanism specificity and pathway-level response polarity. Across 12 perturbation-prediction models, 3 baselines and 10 data splits, expression-similarity metrics were weakly aligned with MFS and selected different model configurations. Mechanism-aware selection improved early drug retrieval in a transcriptome-based drug design evaluation, indicating that MFS provides practical information beyond benchmark reporting. Systematic benchmarking revealed limited fidelity to drug-response signatures across cell-line and source-integrated settings. Frozen single-cell foundation model embeddings produced local, metric-dependent gains rather than universal improvements, and source context substantially reshaped model assessment. Hard-negative tests further showed that plausible perturbation responses can arise from non-specific transcriptional shortcuts. These results show that expression reconstruction is an insufficient proxy for preserving drug-response signatures and establish scDrugPerturb-Bench as a benchmark for mechanism-aware evaluation of single-cell drug perturbation models.