  Minimizing adverse reactions caused by drug-drug interactions has always been
a momentous research topic in clinical pharmacology. Detecting all possible
interactions through clinical studies before a drug is released to the market
is a demanding task. The power of big data is opening up new approaches to
discover various drug-drug interactions. However, these discoveries contain a
huge amount of noise and provide knowledge bases far from complete and
trustworthy ones to be utilized. Most existing studies focus on predicting
binary drug-drug interactions between drug pairs but ignore other interactions.
In this paper, we propose a novel framework, called PRD, to predict drug-drug
interactions. The framework uses the graph embedding that can overcome data
incompleteness and sparsity issues to achieve multiple DDI label prediction.
First, a large-scale drug knowledge graph is generated from different sources.
Then, the knowledge graph is embedded with comprehensive biomedical text into a
common low dimensional space. Finally, the learned embeddings are used to
efficiently compute rich DDI information through a link prediction process. To
validate the effectiveness of the proposed framework, extensive experiments
were conducted on real-world datasets. The results demonstrate that our model
outperforms several state-of-the-art baseline methods in terms of capability
and accuracy.
