Predicting Drug-Target Interactions by Node2vec Node Embedding in Molecular Associations Network

Accurate identification of drug-target interactions (DTIs) is essential for drug development. It not only helps the researchers to understand the mechanism of drug action, but also contributes to the innovative drug discovery and repositioning. However, due to the limitation the high cost and long time, the traditional experimental methods are difficult to be widely applied for DTIs prediction. In this study, we propose an in silico method for predicting drug-target interactions by Node2vec node embedding in molecular associations network (MAN). Specifically, the MAN is constructed by integrating the associations among drug, protein, disease, lncRNA and miRNA. Then, the node2vec embedding method is employed to obtain a behavior feature vector of each node in the network. The traditional attribute feature vector comes from the drug molecular fingerprint and protein sequences. Finally, a random forest (RF) classifier is performed on these features to predict potential drug-target pairs. The experimental results show that the behavior feature could obtain 87.37% accuracy, which is obviously better than the traditional attribute feature. This work is not only more robust and reliable for predicting DTIs, but also provides an alternative way for other biomolecules associations prediction.