KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer’s disease drug repositions
| Autor: | Shudong Wang, Zhenzhen Du, Alfonso Rodríguez-Patón, Tao Song, Mao Ding |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Drug
Artificial neural network Computer science business.industry Deep learning media_common.quotation_subject Drug target 02 engineering and technology Disease Machine learning computer.software_genre Drug repositioning Drug development Knowledge graph Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer media_common |
| Zdroj: | Applied Intelligence. 52:846-857 |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-021-02454-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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