RANEDDI: Relation-aware network embedding for drug-drug interaction prediction

Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

A relation-aware network embedding model RANEDDI is proposed for drug-drug interaction prediction, showing superior performance in binary and multirelational DDIs. By integrating multirelational embedding and relation-aware network structure embedding of drugs, RANEDDI effectively improves DDI prediction accuracy and demonstrates robustness in binary DDI prediction, even with limited labeled data. Available source code at https://github.com/DongWenMin/RANEDDI.