文献类型： 外文期刊

作者： Wang, Huiqing ¹ ; Wang, Jingjing ¹ ; Dong, Chunlin ² ; Lian, Yuanyuan ¹ ; Liu, Dan ¹ ; Yan, Zhiliang ¹ ;

作者机构： 1.Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan, Peoples R China

2.Shanxi Acad Agr Sci, Dryland Agr Res Ctr, Taiyuan, Peoples R China

关键词： drug-target interactions; multiple similarity measures; random walk with restart; positive pointwise mutual information; multimodal deep autoencoder

期刊名称：FRONTIERS IN PHARMACOLOGY （ 影响因子：5.81； 五年影响因子：6.005 ）

ISSN： 1663-9812

年卷期： 2020 年 10 卷

页码：

收录情况： SCI

摘要： Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.