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An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph

文献类型：期刊论文

作者Wan, Xiaozhe2,3; Wu, Xiaolong2,4; Wang, Dingyan2,3; Tan, Xiaoqin5; Liu, Xiaohong1; Fu, Zunyun2; Jiang, Hualiang2,3,6; Zheng, Mingyue2,3; Li, Xutong2,3
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2022-03-12
页码13
关键词compound-protein interaction prediction homogeneous graph end-to-end learning inductive graph neural network
ISSN号1467-5463
DOI10.1093/bib/bbac.073
通讯作者Zheng, Mingyue(myzheng@simm.ac.cn) ; Li, Xutong(lixutong@simm.ac.cn)
英文摘要Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.
WOS关键词TARGET INTERACTION PREDICTION ; DRUG ; INFORMATION ; REPRESENTATION ; DOCKING
资助项目National Natural Science Foundation of China[81773634] ; Strategic Priority Research Program of Chinese Academy of Sciences[SIMM040201] ; Lingang Laboratory[LG202102-01-02] ; 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:000780228900001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/299860]  
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.AlphaMa Inc, 108 Yuxin Rd,Suzhou Ind Pk, Suzhou 215128, Peoples R China
2.Chinese Acad Sci, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
4.East China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China
5.ByteDance AI Lab, Shanghai 201103, Peoples R China
6.ShanghaiTech Univ, Sch Life Sci & Technol, 393 Huaxiazhong Rd, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Wan, Xiaozhe,Wu, Xiaolong,Wang, Dingyan,et al. An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph[J]. BRIEFINGS IN BIOINFORMATICS,2022:13.
APA Wan, Xiaozhe.,Wu, Xiaolong.,Wang, Dingyan.,Tan, Xiaoqin.,Liu, Xiaohong.,...&Li, Xutong.(2022).An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.BRIEFINGS IN BIOINFORMATICS,13.
MLA Wan, Xiaozhe,et al."An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph".BRIEFINGS IN BIOINFORMATICS (2022):13.

入库方式： OAI收割

来源：上海药物研究所

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