Drugdrug 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 drugdrug interactions(DDIs). However, existing DDI prediction approaches are lacking in the following aspects: (1)scalability: they rely heavily on diverse drugrelated features, leading to the unavailability of important features for most of the drugs when it comes to largescale 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 smallscale DDI dataset as well as the largescale one illustrate that our model can achieve higher performance compared to various existing stateoftheart 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.

DrugDrug Interactions Prediction Based on Drug Embedding and Graph AutoEncoder Identification of potential DrugDrug Interactions (DDI) for newly developed drugs is essential in public healthcare. Computational methods of DDI prediction rely on known interactions to learn possible interaction between drug pairs whose interactions are unknown. Past work has used various similarity measures of drugs to predict DDIs. In this paper, we propose an effective approach to DDI Prediction using rich drug representations utilizing multiple knowledge sources. We have used the DrugTarget Interaction (DTI) Network to learn an embedding of drugs by using the metapath2vec algorithm. We have also used drug representation gained from the rich chemical structure representation of drugs using Variational AutoEncoder. The DDI prediction problem is modeled as a link prediction problem in the DDI network containing known interactions. We represent the nodes in the DDI network as their embeddings. We apply a link prediction algorithm based on Graph AutoEncoders to predict additional edges in this network, which are potential interactions. We have evaluated our approach on three benchmark DDI datasets, namely DrugBank, SemMedDB, and BioSNAP. Experimental results demonstrate that the proposed method outperforms the prior methods in terms of several performance metrics (AUC, AUPR, and F1score) on all the datasets. Furthermore, we have also evaluated the role of the individual type of drug representation embeddings in boosting up the performance of DDI Prediction.