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Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery

文献类型：期刊论文

作者Tong, Xiaochu5,6; Qu, Ning5,6; Kong, Xiangtai5,6; Ni, Shengkun5,6; Zhou, Jingyi3,4,6; Wang, Kun2,6; Zhang, Lehan5,6; Wen, Yiming1,5,6; Shi, Jiangshan5,6; Zhang, Sulin5,6
刊名NATURE COMMUNICATIONS
出版日期2024-06-25
卷号15期号:1页码:14
DOI10.1038/s41467-024-49620-3
通讯作者Zhang, Sulin(slzhang@simm.ac.cn) ; Li, Xutong(lixutong@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen's application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine. While chemical-induced transcriptional profiles reveal drug mechanisms, inherent noise limits their utility. Here, authors present TranSiGen, a deep representation learning model that denoises and reconstructs these profiles, demonstrating its efficacy in downstream drug discovery tasks.
WOS关键词CONNECTIVITY MAP ; EXPRESSION ; PROLIFERATION ; SIGNATURES
资助项目National Natural Science Foundation of China[T2225002] ; National Natural Science Foundation of China[82273855] ; National Natural Science Foundation of China[82204278] ; SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program[E2G805H] ; Shanghai Municipal Science and Technology Major Project, National Key Research and Development Program of China[2023YFC2305904] ; Shanghai Municipal Science and Technology Major Project, National Key Research and Development Program of China[2022YFC3400504] ; Youth Innovation Promotion Association CAS[2023296]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001255072700027
出版者NATURE PORTFOLIO
源URL[http://119.78.100.183/handle/2S10ELR8/312119]  
专题新药研究国家重点实验室
通讯作者Zhang, Sulin; Li, Xutong; Zheng, Mingyue
作者单位1.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 310024, Peoples R China
2.Univ Sci & Technol China, Sch Life Sci, Div Life Sci & Med, Hefei 230026, Peoples R China
3.Lingang Lab, Shanghai 200031, Peoples R China
4.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China
5.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
推荐引用方式
GB/T 7714		 Tong, Xiaochu,Qu, Ning,Kong, Xiangtai,et al. Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery[J]. NATURE COMMUNICATIONS,2024,15(1):14.
APA Tong, Xiaochu.,Qu, Ning.,Kong, Xiangtai.,Ni, Shengkun.,Zhou, Jingyi.,...&Zheng, Mingyue.(2024).Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.NATURE COMMUNICATIONS,15(1),14.
MLA Tong, Xiaochu,et al."Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery".NATURE COMMUNICATIONS 15.1(2024):14.

入库方式： OAI收割

来源：上海药物研究所

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