CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction

doi:10.3390/biom11050643

	CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction
	Wang, Xun 1,2; Liu, Dayan 1; Zhu, Jinfu 3; Rodriguez-Paton, Alfonso 4; Song, Tao 1,4
	2021-05-01
发表期刊	BIOMOLECULES
卷号	11 期号:5 页码:9
摘要	The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions' prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23 datasets show that CSConv2d performs better than the original DEEPScreen model in predicting protein-ligand binding affinity, as well as some state-of-the-art DTIs (drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. In practice, the docking results of protein (PDB ID: 5ceo) and ligand (Chemical ID: 50D) and a series of kinase inhibitors are operated to verify the robustness.
关键词	protein-ligand binding affinity 2-D structural CNN spatial attention mechanism
DOI	10.3390/biom11050643
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61873280] ; National Natural Science Foundation of China[61873281] ; National Natural Science Foundation of China[61972416] ; Taishan Scholarship[tsqn201812029] ; Major projects of the National Natural Science Foundation of China[41890851] ; Natural Science Foundation of Shandong Province[ZR2019MF012]
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemistry & Molecular Biology
WOS记录号	WOS:000653400100001
出版者	MDPI
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17562
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Jinfu; Song, Tao
作者单位	1.China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China 2.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 3.Beijing Technol & Business Univ, Sch Econ, Beijing 100048, Peoples R China 4.Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain
推荐引用方式 GB/T 7714	Wang, Xun,Liu, Dayan,Zhu, Jinfu,et al. CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction[J]. BIOMOLECULES,2021,11(5):9.
APA	Wang, Xun,Liu, Dayan,Zhu, Jinfu,Rodriguez-Paton, Alfonso,&Song, Tao.(2021).CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.BIOMOLECULES,11(5),9.
MLA	Wang, Xun,et al."CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction".BIOMOLECULES 11.5(2021):9.