AUTHOR=Yan Xiao-Ying , Yin Peng-Wei , Wu Xiao-Meng , Han Jia-Xin 

TITLE=Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.794205

DOI=10.3389/fphar.2021.794205

ISSN=1663-9812

ABSTRACT=Drug combination therapies are a promising strategy to overcome drug resistance and improve efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, there are some antagonistic DDIs exist, which is the main cause of adverse drug events (ADEs). Precisely predicting the type of drug-drug interactions (DDIs) is important for both drug development and more effective drug combination therapies application. Recently, numerous text mining-based and machine learning-based methods have been developed for predicting drug-drug interactions. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the drug-drug interaction types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using Jaccard coefficient, then implements the random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using multi-model deep autoencoder (MDA). Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of drug-drug interactions. Comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), F1 score, Precision and Recall. In addition, many of the promising types of drug-drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting drug-drug interaction types.