做药物发现或化学基因组学的团队终于有了一个标准化的多源转录组资源——37k化合物、136种细胞环境，直接拿来预训练或评估模型，省去数据清洗的麻烦。
Chem-PerturBridge 是一个整合了37k+化合物、136种细胞环境和125万转录组样本的多数据集资源，覆盖8种实验类型，并统一了标识符、元数据和预处理流程。研究发现，不同数据集间相同化合物的logFC方向和幅度一致性较弱，但方向一致性相对稳定。该资源作为预训练数据，在化合物表示学习任务中优于仅使用L1000数据、Morgan指纹和基线方法。Chem-PerturBridge 支持跨数据集签名一致性的诊断评估，以及异质扰动转录组数据的模型复用。
Chem-PerturBridge: a harmonized compendium of small molecule perturbation transcriptomic effects
Large perturbation models require training data encompassing chemical, cellular, and assay diversity. Current transcriptomic resources for small-molecule modeling, however, are fragmented across technologies, metadata conventions, controls, doses, and preprocessing pipelines. We introduce Chem-PerturBridge, a harmonized multi-dataset resource comprising over 37k compounds, 136 cellular contexts, and 1.25M transcriptomic samples across eight assay types, with standardized identifiers, metadata, and replicate-aware condition-level effects. We use the resource to evaluate matched-condition agreement across datasets and replicate agreement within datasets. Matched same-compound conditions generally show weak agreement in fine-grained logFC rankings and magnitudes across most dataset pairs, often falling below same-context different-compound baselines. In contrast, logFC direction agreement is substantially more stable and usually exceeds these baselines. We further evaluate Chem-PerturBridge as a pretraining resource for compound representation learning. Under a compound-held-out OP3 evaluation split, embeddings pretrained on Chem-PerturBridge improve over L1000-only embeddings, Morgan fingerprints, and the descriptor-free OP3 baseline across metrics. An extensive molecule-holdout evaluation across 11 datasets further shows that models trained on Chem-PerturBridge outperform or match those that are not. Chem-PerturBridge therefore supports both diagnostic evaluation of cross-dataset signature agreement and model-oriented reuse of heterogeneous perturbation transcriptomic data.