Drug-drug Interaction Prediction with Graph Representation Learning

Pharmacological activity of one drug may be altered due to the concomitant administration of another drug, leading to unanticipated drug-drug interactions(DDIs). However, existing DDI prediction approaches are lacking in the following aspects: (1)scalability: they rely heavily on diverse drug-related features, leading to the unavailability of important features for most of the drugs when it comes to large-scale datasets. (2)robustness: they aim to approximate the interaction probability with the integration of diverse features. The model may be sensitive to pairwise similarity information of the test set. In this paper, we explore the promising application of graph representation learning for more accurate DDI prediction, establishing a brand new model to solve the two problems, achieving greater performance and keeping certain interpretability. Our experiments on the small-scale DDI dataset as well as the large-scale one illustrate that our model can achieve higher performance compared to various existing state-of-the-art approaches, which can indicate the scalability of our model. Moreover, our model can find the most important local atoms with the attention mechanism, which conform to domain knowledge with certain interpretability. Furthermore, the robust analysis show that the proposed method is insensitive to the pairwise similarity information of test datasets, and can retrieve interacting drug pairs even though their pairwise similarities are extremely low with a high recall rate and a considerable precision rate.