基于图注意力网络和简单循环单元的化合物-蛋白质交互预测

doi:10.16451/j.cnki.issn1003-6059.202106004

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Abstract The internal covariant shift of the data and the long distance dependence of the sequence data are not taken into account in the existing deep learning based compound protein interaction prediction methods. To solve the problem, a method for compound-protein interaction prediction based on graph attention network and simple recurrent unit is proposed. The graph attention network-gated recurrent unit is introduced to learn the graph-level representation of compound molecules, the multi-layer-simple recurrent unit is employed to learn feature vector representation of amino acid subsequences, and multilayer-feed-forward network is utilized to predict compound-protein interactions. Experiments show that the evaluation indexes of the proposed method are improved on 2 public datasets, and the effectiveness of the proposed method is verified.

Key words： Compound-Protein Interaction Graph Attention Network Simple Recurrent Unit Gated Recurrent Unit Attention Mechanism

Received: 12 November 2020

ZTFLH:

TP 183

Fund:National Natural Science Foundation of China(No.61907011)

Corresponding Authors: LI Shuhong, Ph.D., professor. Her research interests include intelligent information processing.

About author:: JIA Lin, master student. Her research interests include deep learning.

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Cite this article:

LI Shuhong,JIA Lin. Compound-Protein Interaction Prediction Based on Graph Attention Network and Simple Recurrent Unit[J]. , 2021, 34(6): 522-531.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202106004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I6/522

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