Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
文献类型：期刊论文
| 作者 | Qi, Xiaoning1,2; Zhao, Lianhe1,2; Tian, Chenyu3,4; Li, Yueyue3,4; Chen, Zhen-Lin2,5; Huo, Peipei6; Chen, Runsheng7; Liu, Xiaodong8; Wan, Baoping1; Yang, Shengyong3,4 |
| 刊名 | NATURE COMMUNICATIONS
 |
| 出版日期 | 2024-10-26 |
| 卷号 | 15期号:1页码:19 |
| DOI | 10.1038/s41467-024-53457-1 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001345548100011 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39469]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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