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

doi:10.1038/s41467-024-53457-1

	Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
	Qi, Xiaoning 1,2; Zhao, Lianhe 1,2; Tian, Chenyu 3,4; Li, Yueyue 3,4; Chen, Zhen-Lin 2,5; Huo, Peipei 6; Chen, Runsheng 7; Liu, Xiaodong 8; Wan, Baoping 1; Yang, Shengyong 3,4; Zhao, Yi 1,2
	2024-10-26
发表期刊	NATURE COMMUNICATIONS
卷号	15 期号:1 页码:19
摘要	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. Understanding transcriptional responses to chemical perturbations is crucial for drug discovery. Here, authors present PRnet, a deep generative model that predicts gene responses to novel chemical perturbations, enabling in-silico drug screening and the identification of candidate compounds for various diseases.
DOI	10.1038/s41467-024-53457-1
收录类别	SCI
语种	英语
资助项目	The National Key RD Program of China (2021YFC2500203), The National Natural Science Foundation of China (32341019, 32070670), Ningbo major project for high-level medical and healthcare teams (2023030615), Beijing Natural Science Foundation Haidian Origina[2022YFF1203303] ; National Key R&D Program of China[32341019] ; National Key R&D Program of China[32070670] ; National Natural Science Foundation of China[2023030615] ; Ningbo major project for high-level medical and healthcare teams[L222007] ; Beijing Natural Science Foundation Haidian Origination and Innovation Joint Fund[2024Z229] ; Ningbo Science and Technology Innovation Yongjiang 2035 Project[GZNL2023A03001] ; Major Project of Guangzhou National Laboratory[KF2422-93] ; Open Project of National Key Laboratory of Oncology Systems Medicine
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001345548100011
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39470
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yang, Shengyong; Zhao, Yi
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Sichuan Univ, West China Hosp, Canc Ctr, Dept Biotherapy, Chengdu, Sichuan, Peoples R China 4.Sichuan Univ, West China Hosp, State Key Lab Biotherapy, Chengdu, Sichuan, Peoples R China 5.Chinese Acad Sci, Chinese Acad Sci CAS, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 6.Luoyang Inst Informat Technol Ind, Luoyang, Henan, Peoples R China 7.Sichuan Univ, West China Hosp, Chengdu, Sichuan, Peoples R China 8.Univ Chinese Acad Sci, Nanjing, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Qi, Xiaoning,Zhao, Lianhe,Tian, Chenyu,et al. Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery[J]. NATURE COMMUNICATIONS,2024,15(1):19.
APA	Qi, Xiaoning.,Zhao, Lianhe.,Tian, Chenyu.,Li, Yueyue.,Chen, Zhen-Lin.,...&Zhao, Yi.(2024).Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery.NATURE COMMUNICATIONS,15(1),19.
MLA	Qi, Xiaoning,et al."Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery".NATURE COMMUNICATIONS 15.1(2024):19.