Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model

Abstract

Predicting drug-target interactions (DTIs) is crucial in innovative drug discovery, drug repositioning and other fields. However, there are many shortcomings for predicting DTIs using traditional biological experimental methods, such as the high-cost, timeconsumption, low efficiency, and so on, which make these methods difficult to widely apply. As a supplement, the in silico method can provide helpful information for predictions of DTIs in a timely manner. In this work, a deep walk embedding method is developed for predicting DTIs from a multi-molecular network. More specifically, a multi-molecular network, also called molecular associations network, is constructed by integrating the associations among drug, protein, disease, lncRNA, and miRNA. Then, each node can be represented as a behavior feature vector by using a deep walk embedding method. Finally, we compared behavior features with traditional attribute features on an integrated dataset by using various classifiers. The experimental results revealed that the behavior feature could be performed better on different classifiers, especially on the random forest classifier. It is also demonstrated that the use of behavior information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work is not only extremely suitable for predicting DTIs, but also provides a new perspective for the prediction of other biomolecules' associations.

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

“…Therefore, they proposed a novel framework for identifying DTIs that acquire latent features from DTI network. Another recent work was introduced in [ 54 ] that uses a deep-walk embedding concept to predict DTIs from a molecular association’s network. This network is constructed by combining the associations among protein, drug, disease, micro RNA and long non-coding RNA (lncRNA).…”

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

“…Luo et al [ 17 ] constructed a heterogeneous network to predict the potential DTIs by integrating the information of multiple drugs. Chen et al [ 18 ] and Ji et al [ 19 ] proposed a multi-molecular network model based on network embedding to predict novel DTIs. Liu et al [ 20 ] proposed a model called NRLMF, which calculates the score of DTIs through logical matrix decomposition, where the properties of the drug and target are expressed in terms of their specificity.…”

MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning

“…Recently, a large number of computing methods based on drug-disease associations prediction have been proposed (Huang et al, 2013 ; Li et al, 2016 ; Zickenrott et al, 2016 ; Zhang et al, 2017a ; Xue et al, 2018 ; Yella et al, 2018 ; Cui et al, 2019 ; Xuan et al, 2019 ; Chen et al, 2020 ; Jarada et al, 2020 ). Gottlieb et al ( 2011 ) proposed the prediction method based on the computational similarity framework between drug-drug similarity and disease-disease similarity and predict unknown correlations by constructing similar characteristics of recently known drug-disease associations.…”

“…Most of the existing drugs are used to discover the relationship between potential drugs and diseases by extracting similarities between drugs and diseases (Li and Lu, 2012 ; Zhang et al, 2014 , 2017b , 2018 ; Luo et al, 2016 ). Chen et al ( 2020 ) used network embedding and traditional attributes to predict drug targets by integrating the correlation between various molecules. According to research, graph neural network has been widely used in related biological and medical fields (Li et al, 2020 ; Wang et al, 2020 ; Yue et al, 2020 ).…”