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

Jiajie Peng, Yuxian Wang, Jiaojiao Guan, Jingyi Li, Ruijiang Han, Jianye Hao, Zhongyu Wei, Xuequn Shang

科研成果: 期刊稿件文章同行评审

139 引用 (Scopus)

摘要

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.

源语言英语
文章编号bbaa430
期刊Briefings in Bioinformatics
22
5
DOI
出版状态已出版 - 1 9月 2021

指纹

探究 'An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction' 的科研主题。它们共同构成独一无二的指纹。

引用此