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KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer’s disease drug repositions

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Abstract

Drug repositioning, which recommends approved drugs to potential targets by predicting drug-target interactions (DTIs), can save the cost and shorten the period of drug development. In this work, we propose a novel knowledge graph based deep learning method, named KG-DTI, for DTIs predictions. Specifically, a knowledge graph of 29,607 positive drug-target pairs is constructed by DistMult embedding strategy. A Conv-Conv module is proposed to extract features of drug-target pairs (DTPs), which is followed by a fully connected neural network for DTIs calculation. Data experiments are conducted on randomly chosen 11,840 positive and negative samples. It is obtained that KG-DTI achieves average ACC by 88.0%, F1-Score by 87.7%, AUROC by 94.3% and AUPR by 95% in five-fold cross-validation. In practice, KG-DTI is applied to reposition drugs to Alzheimer’s disease (AD) by Apolipoprotein E target. As results, it is found that seven of the top ten recommended drugs have been used in clinic practice or with literature supported useful to AD. Ligand-target docking results show that the top one recommended drug can dock with Apolipoprotein E significantly, which gives vital hints in repositioning potential drug to AD treatment.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61873280, 61972416), Taishan Scholarship (tsqn201812029), Major projects of the National Natural Science Foundation of China (Grant No. 41890851), Natural Science Foundation of Shandong Province (No. ZR2019MF012), Fundamental Research Funds for the Central Universities (19CX05003A-6).

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Authors and Affiliations

  1. College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China

    Shudong Wang, Zhenzhen Du & Tao Song

  2. Department of Neurology Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, China

    Mao Ding

  3. Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Boadilla del Monte, 28660, Madrid, Spain

    Alfonso Rodriguez-Paton & Tao Song

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  1. Shudong Wang
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  2. Zhenzhen Du
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  3. Mao Ding
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  5. Tao Song
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Correspondence to Mao Ding or Tao Song.

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Wang, S., Du, Z., Ding, M. et al. KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer’s disease drug repositions. Appl Intell 52, 846–857 (2022). https://doi.org/10.1007/s10489-021-02454-8

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