Predicting Drug-Drug Interactions with Graph Attention Network

Jianjia Wang, Cheng Guo, Xing Wu*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)


Predicting Drug-Drug Interactions (DDIs) is usually a time-consuming and labour-intensive task. The undetected adverse interactions between drugs are a common cause of medical injuries. Recently, with the assistance of deep learning algorithms, the accuracy of prediction in DDIs significantly improved. However, previous methods do not take well into account both adapting to datasets and extracting neighbourhood information on the graph-structured data. In this study, we propose a new framework to fill this gap, named Interaction Prediction Graph Attention Network(IPGAT). This framework consists of two modules, i.e., the embedding module and the prediction module. Inspired by the Graph Embedding and Graph Attention Networks, the embedding module extracts features from graph-structured data with high-order neighbourhoods. Then, it directly transfers to the prediction module without an intermediate process. Our proposed IPGAT presents advantages compared to existing DDI prediction methods. Experimental results on the public DrugBank dataset reveal that IPGAT significantly outperforms the state-of-the-art methods such as AMF&AMFP, Conv-LSTM, Graph Auto-Encoder, etc. The corresponding results increase 6.9% in AUROC and at least 8.5% in AUPR for the retrospective experiment. Further studies verify the efficacy of multi-layer and multi-head in the model. The codes are available at

Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781665490627
Publication statusPublished - 2022
Externally publishedYes
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: 21 Aug 202225 Aug 2022

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference26th International Conference on Pattern Recognition, ICPR 2022


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