An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction. Issue 5 (1st February 2021)

• Record Type: Journal Article
• Authors: Peng, Jiajie; Wang, Yuxian; Guan, Jiaojiao; Li, Jingyi; Han, Ruijiang; Hao, Jianye; Wei, Zhongyu; Shang, Xuequn
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-02-01
• Subjects: drug–target interaction prediction -- heterogeneous network -- end-to-end learning -- graph convolutional networks; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa430
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Accurately identifying potential drug–target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI identification is still timeconsuming and expensive. Therefore, it is of great significance to develop effective computational methods for identifying DTIs. In this paper, we develop a novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end-to-end graph (EEG)-DTI. Given a heterogeneous network containing multiple types of biological entities (i.e. drug, protein, disease, side-effect), EEG-DTI learns the low-dimensional feature representation of drugs and targets using a graph convolutional networks-based model and predicts DTIs based on the learned features. During the training process, EEG-DTI learns the feature representation of nodes in an end-to-end mode. The evaluation test shows that EEG-DTI performs better than existing state-of-art methods. The data and source code are available at: https://github.com/MedicineBiology-AI/EEG-DTI .