Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.

了解对化学扰动的转录反应是药物发现的核心，但对疾病-化合物组合进行详尽的实验筛选是不可行的。为了克服这一限制，我们在这里介绍了 PRnet，这是一种扰动条件的深度生成模型，可预测对新型化学扰动的转录反应，这些扰动从未在批量和单细胞水平上进行过实验扰动。评估表明，PRnet 在预测新化合物、通路和细胞系的反应方面优于其他方法。PRnet 能够根据基因特征对疾病进行基因水平反应解释和计算机药物筛选。PRnet 进一步鉴定并实验验证了针对小细胞肺癌和结直肠癌的新型候选化合物。最后，PRnet 生成了扰动谱的大规模整合图谱，涵盖 88 个细胞系、52 个组织和各种化合物库。PRnet 提供强大且可扩展的候选药物推荐工作流程，并成功推荐了 233 种疾病的候选药物。总体而言，PRnet 是一种有效且有价值的基于基因的治疗学筛选工具。