An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction

• Published in: Briefings in bioinformatics Vol. 22; no. 5
• Main Authors: Peng, Jiajie, Wang, Yuxian, Guan, Jiaojiao, Li, Jingyi, Han, Ruijiang, Hao, Jianye, Wei, Zhongyu, Shang, Xuequn
• Format: Journal Article
• Language: English
• Published: England 02.09.2021
• Subjects: end-to-end learning; graph convolutional networks; heterogeneous network; drug–target interaction prediction
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbaa430

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.