ShanghaiTech University Knowledge Management System
| Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery | |
| 2024-06-25 | |
| Source Publication | NATURE COMMUNICATIONS
 |
| EISSN | 2041-1723 |
| Volume | 15Issue:1 |
| Status | 已发表 |
| DOI | 10.1038/s41467-024-49620-3 |
| Abstract | ["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."] |
| URL | 查看原文 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China["T2225002","82273855","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","2022YFC3400504"] ; Youth Innovation Promotion Association CAS[2023296] |
| WOS Research Area | Science & Technology - Other Topics |
| WOS Subject | Multidisciplinary Sciences |
| WOS ID | WOS:001255072700027 |
| Publisher | NATURE PORTFOLIO |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/401415 |
| Collection | 物质科学与技术学院 物质科学与技术学院_博士生 |
| Corresponding Author | Zhang, Sulin; Li, Xutong; Zheng, Mingyue |
| Affiliation | 1.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China 4.Lingang Lab, Shanghai 200031, Peoples R China 5.Univ Sci & Technol China, Sch Life Sci, Div Life Sci & Med, Hefei 230026, Peoples R China 6.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 310024, Peoples R China |
| Recommended Citation 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). |
| 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). |
| MLA | Tong, Xiaochu,et al."Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery".NATURE COMMUNICATIONS 15.1(2024). |
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