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.

Structural Network Embedding using Multimodal Deep Autoencoders for Predicting Drugdrug Interactions Predicting drugdrug interactions (DDIs) is crucial for patient safety and public health. The existing DDI prediction methods mainly fall into three categories: knowledgebased, similaritybased and networkbased. Most recently, studies have demonstrated that integrating heterogeneous drug features is significantly important for developing highaccuracy prediction models, but it also brings many new challenges, i.e. heterogeneous properties, nonlinear relations and incomplete data. In this paper, we propose a multimodal deep autoencoders based drug representation learning method for the DDI prediction, abbreviated as DDIMDAE. The proposed method learns unified representations of drugs simultaneously from multiple drug feature networks using multimodal deep autoencoders. Then we adopt several operators on the learned drug embeddings to represent drugdrug pairs, and utilize the random forest to train models for the DDI prediction. Experimental results show that DDIMDAE effectively learns the representations of drugs by fusing diverse information, and outperforms the other stateoftheart benchmark methods. More importantly, DDIMDAE works even for drugs without any known interaction.