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SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

文献类型：期刊论文

作者Wang, Xun1,2; Liu, Jiali1; Zhang, Chaogang1; Wang, Shudong1
刊名INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
出版日期2022-04-01
卷号23期号:7页码:13
关键词deep learning compound-protein interactions compound properties protein preperties IC50 value
DOI10.3390/ijms23073780
英文摘要Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI's deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R-2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R-2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.
资助项目Natural Science Foundation of China[61873280] ; Natural Science Foundation of China[61672033] ; Natural Science Foundation of China[61672248] ; Natural Science Foundation of China[61972416] ; Taishan Scholarship[tsqn201812029] ; Natural Science Foundation of Shandong Province[ZR2019MF012] ; Foundation of Science and Technology Development of Jinan[201907116] ; Fundamental Research Funds for the Central Universities[18CX02152A] ; Fundamental Research Funds for the Central Universities[19CX05003A-6] ; Spanish project[PID2019-106960GB-I00] ; Juan de la Cierva[IJC2018-038539-I]
WOS研究方向Biochemistry & Molecular Biology ; Chemistry
语种英语
出版者MDPI
WOS记录号WOS:000781958900001
源URL[http://119.78.100.204/handle/2XEOYT63/18906]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shudong
作者单位1.China Univ Petr, Coll Comp Sci & Technol, Qingdao 266555, Peoples R China
2.Univ Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xun,Liu, Jiali,Zhang, Chaogang,et al. SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2022,23(7):13.
APA Wang, Xun,Liu, Jiali,Zhang, Chaogang,&Wang, Shudong.(2022).SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,23(7),13.
MLA Wang, Xun,et al."SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 23.7(2022):13.

入库方式： OAI收割

来源：计算技术研究所

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