Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Nat Commun. 2024 Oct 26;15(1):9256. doi: 10.1038/s41467-024-53457-1.
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.
理解化学干扰下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，我们在这里引入了 PRnet，这是一种扰动条件下的深度生成模型，可以预测从未在批量和单细胞水平上进行过实验扰动的新型化学扰动的转录反应。评估表明，PRnet 在预测新型化合物、途径和细胞系的反应方面优于其他方法。PRnet 能够基于基因特征对疾病进行基因水平的反应解释和计算机药物筛选。PRnet 进一步鉴定并通过实验验证了针对小细胞肺癌和结直肠癌的新型化合物候选物。最后，PRnet 生成了一个大规模的扰动谱综合图谱，涵盖 88 种细胞系、52 种组织和各种化合物库。PRnet 提供了一个强大且可扩展的候选推荐工作流程，并成功为 233 种疾病推荐了药物候选物。总的来说，PRnet 是一种有效的、有价值的基于基因的治疗筛选工具。
