HNEDTI: Prediction of drug-target interaction based on heterogeneous network embedding

Identifying drug-target 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 drug-target interactions with rare known drug-target interactions. In this work, we propose a heterogeneous network embedding model to predict drug-target interactions, called HNEDTI. Based on the assumption that similar drugs share similar patterns of relationships with target proteins, we integrate the drug-drug similarity network, target-target similarity network and known drug-target 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 meta-paths. The low dimensional feature representation vectors of drugs and targets are applied to random forest model to predict whether the given drug-target 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.