Multi-instance learning of graph neural networks for aqueous pK(a) prediction

doi:10.1093/bioinformatics/btab714

	Multi-instance learning of graph neural networks for aqueous pK(a) prediction
	Xiong, Jiacheng 1,2; Li, Zhaojun 3; Wang, Guangchao 4; Fu, Zunyun 1; Zhong, Feisheng 1,2; Xu, Tingyang 5; Liu, Xiaomeng 1,2; Huang, Ziming 1,2; Liu, Xiaohong1,3,6,7 ; Chen, Kaixian 1,2; Jiang, Hualiang1,2,6,7 ; Zheng, Mingyue 1,2
	2022-02-01
发表期刊	BIOINFORMATICS (IF:4.4[JCR-2023],7.6[5-Year])
ISSN	1367-4803
EISSN	1460-2059
卷号	38 期号:3
发表状态	已发表
DOI	10.1093/bioinformatics/btab714
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."]
URL	查看原文
收录类别	SCI ; SCIE
语种	英语
资助项目	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
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000743386000024
出版者	OXFORD UNIV PRESS
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/153558
专题	生命科学与技术学院_博士生免疫化学研究所_特聘教授组_蒋华良组
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
通讯作者单位	免疫化学研究所; 生命科学与技术学院
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).
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).
MLA	Xiong, Jiacheng,et al."Multi-instance learning of graph neural networks for aqueous pK(a) prediction".BIOINFORMATICS 38.3(2022).