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GraphCPIs: A novel graph-based computational model for potential compound-protein interactions by Zhan-Heng Chen; Bo-Wei Zhao; Jian-Qiang Li; Zhen-Hao Guo; Zhu-Hong You is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is GraphCPIs: A novel graph-based computational model for potential compound-protein interactions about?

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 surpasse

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Author

Zhan-Heng Chen; Bo-Wei Zhao; Jian-Qiang Li; Zhen-Hao Guo; Zhu-Hong You

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)