GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.
GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.
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DOI：
10.1016/j.omtn.2023.04.030
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发表时间：
2023-06-13
期刊：
影响因子：
--
通讯作者：
You, Zhu-Hong
中科院分区：
文献类型：
--
作者：
Chen, Zhan-Heng;Zhao, Bo-Wei;Li, Jian-Qiang;Guo, Zhen-Hao;You, Zhu-Hong
Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins. Chen and colleagues propose a GraphCPIs model to predict the drug compound-protein interactions. The feature representation information of nodes can be obtained by using the graph convolutional network and Grarep embedding model. The stacked two kinds of features are fed into an extreme gradient boosting (XGBoost) classifier to predict potential CPIs.
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影响因子：
14.9
作者：
Kim S;Chen J;Cheng T;Gindulyte A;He J;He S;Li Q;Shoemaker BA;Thiessen PA;Yu B;Zaslavsky L;Zhang J;Bolton EE
通讯作者：
Bolton EE
DOI：
10.1021/c160017a018
发表时间：
1965-01-01
期刊：
JOURNAL OF CHEMICAL DOCUMENTATION
影响因子：
--
作者：
MORGAN, HL
通讯作者：
MORGAN, HL
影响因子：
5.8
作者：
Sun, Chang;Cao, Yangkun;Liu, Jian
通讯作者：
Liu, Jian
影响因子：
9.5
作者：
Bagherian M;Sabeti E;Wang K;Sartor MA;Nikolovska-Coleska Z;Najarian K
通讯作者：
Najarian K
影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH