Drug target inference by mining transcriptional data using a novel graph convolutional network framework
文献类型：期刊论文
| 作者 | Zhong, Feisheng2,3; Wu, Xiaolong2,4; Yang, Ruirui1,2,3,6; Li, Xutong2,3; Wang, Dingyan2,3; Fu, Zunyun2,5; Liu, Xiaohong1,2,6; Wan, XiaoZhe2,3; Yang, Tianbiao2,3; Fan, Zisheng2,5 |
| 刊名 | PROTEIN & CELL
 |
| 出版日期 | 2021-10-22 |
| 页码 | 21 |
| ISSN号 | 1674-800X |
| 关键词 | drug target inference transcriptomics deep learning experimental verification |
| DOI | 10.1007/s13238-021-00885-0 |
| 通讯作者 | Zhang, Sulin(slzhang@simm.ac.cn) ; Jiang, Hualiang(hljiang@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn) |
| 英文摘要 | A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest. |
| WOS关键词 | CONNECTIVITY MAP ; GENE TARGETS ; INHIBITOR ; LIGAND ; CYCLOPHILIN ; POTENT ; DESTABILIZATION ; IDENTIFICATION ; CHALLENGES ; SIGNATURES |
| 资助项目 | National Natural Science Foundation of China[81773634] ; National Natural Science Foundation of China[81903639] ; National Science & Technology Major Project Key New Drug Creation and Manufacturing Program, China[2018ZX09711002] ; Personalized Medicines-Molecular Signature-based Drug Discovery and Development, Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12050201] ; Shanghai Sailing Program[19YF1457800] |
| WOS研究方向 | Cell Biology |
| 语种 | 英语 |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:000710045500002 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/298323]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Zhang, Sulin; Jiang, Hualiang; Zheng, Mingyue |
| 作者单位 | 1.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 200031, Peoples R China 2.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.East China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China 5.Nanjing Univ Chinese Med, Nanjing 210023, Peoples R China 6.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhong, Feisheng,Wu, Xiaolong,Yang, Ruirui,et al. Drug target inference by mining transcriptional data using a novel graph convolutional network framework[J]. PROTEIN & CELL,2021:21. |
| APA | Zhong, Feisheng.,Wu, Xiaolong.,Yang, Ruirui.,Li, Xutong.,Wang, Dingyan.,...&Zheng, Mingyue.(2021).Drug target inference by mining transcriptional data using a novel graph convolutional network framework.PROTEIN & CELL,21. |
| MLA | Zhong, Feisheng,et al."Drug target inference by mining transcriptional data using a novel graph convolutional network framework".PROTEIN & CELL (2021):21. |
入库方式： OAI收割
来源：上海药物研究所
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