HNEDTI: Prediction of drugtarget interaction based on heterogeneous network embedding Identifying drugtarget interactions (DTIs) is an important task in drug discovery. Various computational models have been proposed to predict potential association between drugs and targets. However, it is still a great challenge to accurately predict the potential drugtarget interactions with rare known drugtarget interactions. In this work, we propose a heterogeneous network embedding model to predict drugtarget interactions, called HNEDTI. Based on the assumption that similar drugs share similar patterns of relationships with target proteins, we integrate the drugdrug similarity network, targettarget similarity network and known drugtarget interactions into a heterogeneous network. HNEDTI can learn more accurate feature representation of drugs and targets by extract both local and global information of the heterogeneous network from different lengths of metapaths. The low dimensional feature representation vectors of drugs and targets are applied to random forest model to predict whether the given drugtarget pair has an interaction. The evaluation on four benchmark datasets (Enzyme, Ion Channel, GPCR and Nuclear Receptor) shows that our method HNEDTI outperforms the previous methods.

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.