Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery

Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of diseasecompound 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.解析化学扰动下的转录应答是药物发现的核心环节，但针对疾病-化合物组合开展全面实验筛选并不可行。为克服这一局限，本文提出PRnet——一种扰动条件化深度生成模型（perturbation-conditioned deep generative model），可预测从未在批量和单细胞层面被实验性施加过扰动的新型化学扰动所对应的转录应答。评估结果显示，PRnet在预测新型化合物、信号通路及细胞系对应的应答方面，性能优于其他替代方法。PRnet可实现基于基因签名（gene signatures）的疾病基因层面应答解析与虚拟药物筛选（in-silico drug screening）。PRnet还可针对小细胞肺癌与结直肠癌，识别并通过实验验证新型候选化合物。此外，PRnet可生成大规模扰动谱整合图谱，涵盖88种细胞系、52种组织及各类化合物库。PRnet提供了一套鲁棒且可扩展的候选化合物推荐工作流，并成功为233种疾病推荐了候选药物。总体而言，PRnet是一款用于基于基因的疗法筛选的高效且极具价值的工具。