MHADTI: predicting drug–target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms. Issue 6 (14th October 2022)

• Record Type: Journal Article
• Authors: Tian, Zhen; Peng, Xiangyu; Fang, Haichuan; Zhang, Wenjie; Dai, Qiguo; Ye, Yangdong
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-10-14
• Subjects: data fusion -- multiview heterogeneous information network embedding -- hierarchical attention mechanisms -- drug–target interaction prediction; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac434
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: Discovering the drug–target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently. Results: In this study, we develop a novel method via M ultiview heterogeneous information network embedding with H ierarchical A ttention mechanisms to discover potential D rug–T arget I nteractions (MHADTI ). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug–target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs. Availability and implementation: https://github.com/pxystudy/MHADTI