KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling
文献类型：期刊论文
| 作者 | Ren, Qun5,6; Qu, Ning5; Sun, Jingjing5; Zhou, Jingyi4; Liu, Jin3; Ni, Lin5,6; Tong, Xiaochu5; Zhang, Zimei5; Kong, Xiangtai5; Wen, Yiming2 |
| 刊名 | BRIEFINGS IN BIOINFORMATICS
 |
| 出版日期 | 2024 |
| 卷号 | 25期号:1页码:15 |
| 关键词 | kinome profiling kinase inhibitors polypharmacology deep learning meta-learning |
| ISSN号 | 1467-5463 |
| DOI | 10.1093/bib/bbad461 |
| 通讯作者 | Zheng, Mingyue(myzheng@simm.ac.cn) ; Li, Xutong(lixutong@simm.ac.cn) |
| 英文摘要 | Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape. |
| WOS关键词 | BIOLOGICAL EVALUATION ; KINASE INHIBITORS ; DESIGN ; DERIVATIVES ; TARGETS |
| 资助项目 | National Key Research and Development Program of China[2022YFC3400504] ; National Natural Science Foundation of China[T2225002] ; National Natural Science Foundation of China[82273855] ; National Natural Science Foundation of China[82204278] ; Lingang Laboratory[LG202102-01-02] ; SIMM-SHUTCMTraditional Chinese Medicine Innovation Joint Research Program[E2G805H] ; China Postdoctoral Science Foundation[2022 M720153] ; Shanghai Municipal Science and Technology Major Project |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:001173375300093 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/310145]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Zheng, Mingyue; Li, Xutong |
| 作者单位 | 1.Lingang Lab, Lingang, Peoples R China 2.Hangzhou Inst Adv Study, Pharmaceut Sci & Technol, Hangzhou, Peoples R China 3.Zhejiang Univ, Coll Pharmaceut Sci, Hangzhou, Peoples R China 4.ShanghaiTech Univ, Shanghai, Peoples R China 5.Shanghai Inst Mat Med, Shanghai, Peoples R China 6.Nanjing Univ Chinese Med, Nanjing, Peoples R China 7.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 8.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & DesignCenter, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 9.Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 330106, Peoples R China 10.Nanjing Univ Chinese Med, 138 Xianlin Rd, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ren, Qun,Qu, Ning,Sun, Jingjing,et al. KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling[J]. BRIEFINGS IN BIOINFORMATICS,2024,25(1):15. |
| APA | Ren, Qun.,Qu, Ning.,Sun, Jingjing.,Zhou, Jingyi.,Liu, Jin.,...&Li, Xutong.(2024).KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.BRIEFINGS IN BIOINFORMATICS,25(1),15. |
| MLA | Ren, Qun,et al."KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling".BRIEFINGS IN BIOINFORMATICS 25.1(2024):15. |
入库方式： OAI收割
来源：上海药物研究所
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