构建化学与基因扰动翻译的基准，评估其在细胞靶点环境中的匹配性。
Chem2Gen-Bench: Benchmarking Chemical-to-Genetic Translation in Perturbation Response Space
- 构建包含26万条化学与110万条基因扰动数据的基准
- 发现背景调整能提升相似性与检索成功率关联性
- 验证基础模型嵌入未显著优于基因差值基线
虚拟细胞和扰动模型被广泛用于生物医学发现中的细胞响应预测，但化学与基因扰动并非天然可互换。现有评估多孤立研究化学响应或基因扰动预测，忽视了目标匹配的化学到基因翻译能力。本文提出Chem2Gen-Bench，包含260,084条化学扰动与1,099,045条基因扰动数据，按细胞-靶点上下文组织，评估配对对齐、检索、协变量关联、特征空间及基础模型嵌入。在匹配上下文中，翻译保真度可测量但存在异质性；背景调整增强了配对相似性与检索成功之间的关联，但成对测试中调整后平均检索成功率下降。在目标匹配的K562审计中，所评估的基础模型嵌入未持续优于gene-delta基线。Chem2Gen-Bench为检验化学与基因扰动在共享靶点处是否对齐、表示增益是否有匹配扰动证据支持提供了可审计框架。
原文摘要 · Abstract (English)
Virtual-cell and perturbation models are increasingly used to predict cellular responses for biomedical discovery, but chemical and genetic perturbations are not automatically interchangeable. Existing evaluations often study chemical response prediction or genetic perturbation prediction separately, leaving target-matched chemical-to-genetic translation under-tested. We introduce Chem2Gen-Bench, a benchmark comprising 260,084 chemical and 1,099,045 genetic perturbation profiles organized into cell-target contexts, and evaluate pairwise alignment, retrieval, protocol covariate associations, feature spaces, and foundation-model embeddings. Across matched contexts, translation fidelity is measurable but heterogeneous; background adjustment increases the association between pairwise similarity and retrieval success, while paired tests show lower mean retrieval success after adjustment under the evaluated settings. In a target-matched K562 audit, the evaluated foundation-model embeddings did not consistently improve over gene-delta baselines. Chem2Gen-Bench provides an auditable framework for testing when chemical and genetic perturbations align around shared targets and when representation gains are supported by matched perturbation evidence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。