A heterogeneous network embedding framework for predicting similarity-based drug-target interactions

Abstract

Accurate prediction of drug-target interactions (DTIs) through biological data can reduce the time and economic cost of drug development. The prediction method of DTIs based on a similarity network is attracting increasing attention. Currently, many studies have focused on predicting DTIs. However, such approaches do not consider the features of drugs and targets in multiple networks or how to extract and merge them. In this study, we proposed a Network EmbeDding framework in mulTiPlex networks (NEDTP) to predict DTIs. NEDTP builds a similarity network of nodes based on 15 heterogeneous information networks. Next, we applied a random walk to extract the topology information of each node in the network and learn it as a low-dimensional vector. Finally, the Gradient Boosting Decision Tree model was constructed to complete the classification task. NEDTP achieved accurate results in DTI prediction, showing clear advantages over several state-of-the-art algorithms. The prediction of new DTIs was also verified from multiple perspectives. In addition, this study also proposes a reasonable model for the widespread negative sampling problem of DTI prediction, contributing new ideas to future research. Code and data are available at https://github.com/LiangYu-Xidian/NEDTP.

Keywords: drug-target interaction; heterogeneous network; machine learning; network embedding.