Multi-instance learning of graph neural networks for aqueous pK(a) prediction
文献类型：期刊论文
| 作者 | Xiong, Jiacheng1,2; Li, Zhaojun3; Wang, Guangchao4; Fu, Zunyun1; Zhong, Feisheng1,2; Xu, Tingyang5; Liu, Xiaomeng1,2; Huang, Ziming1,2; Liu, Xiaohong1,3,6,7; Chen, Kaixian1,2
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| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 38期号:3页码:792-798 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btab714 |
| 通讯作者 | Jiang, Hualiang(hljiang@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn) |
| 英文摘要 | Motivation: The acid dissociation constant (pK(a)) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pK(a) is intricate and time-consuming, especially for the exact determination of micro-pK(a) information at the atomic level. Hence, a fast and accurate prediction of pK(a) values of chemical compounds is of broad interest. Results: Here, we compiled a large-scale pK(a) dataset containing 16 595 compounds with 17 489 pK(a) values. Based on this dataset, a novel pK(a) prediction model, named Graph-pK(a), was established using graph neural networks. Graph-pK(a) performed well on the prediction of macro-pK(a) values, with a mean absolute error around 0.55 and a coefficient of determination around 0.92 on the test dataset. Furthermore, combining multi-instance learning, Graph-pK(a) was also able to automatically deconvolute the predicted macro-pK(a) into discrete micro-pK(a) values. |
| WOS关键词 | DRUGS |
| 资助项目 | Shanghai Municipal Science and Technology Major Project ; National Natural Science Foundation of China[81773634] ; Tencent AI Lab Rhino-Bird Focused Research Program[JR202002] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000743386000024 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/300510]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Jiang, Hualiang; Zheng, Mingyue |
| 作者单位 | 1.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 2.Univ Chinese Acad Sci, Coll Pharm, Beijing 100049, Peoples R China 3.Suzhou Alphama Biotechnol Co Ltd, Dev Dept, Suzhou 215000, Peoples R China 4.Dezhou Univ, Coll Comp & Informat Engn, Dezhou City 253023, Peoples R China 5.Tencent, Tencent AI Lab, Shenzhen 518057, Peoples R China 6.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 200031, Peoples R China 7.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiong, Jiacheng,Li, Zhaojun,Wang, Guangchao,et al. Multi-instance learning of graph neural networks for aqueous pK(a) prediction[J]. BIOINFORMATICS,2022,38(3):792-798. |
| APA | Xiong, Jiacheng.,Li, Zhaojun.,Wang, Guangchao.,Fu, Zunyun.,Zhong, Feisheng.,...&Zheng, Mingyue.(2022).Multi-instance learning of graph neural networks for aqueous pK(a) prediction.BIOINFORMATICS,38(3),792-798. |
| MLA | Xiong, Jiacheng,et al."Multi-instance learning of graph neural networks for aqueous pK(a) prediction".BIOINFORMATICS 38.3(2022):792-798. |
入库方式： OAI收割
来源：上海药物研究所
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