A Network-Based Embedding Method for Drug-Target Interaction Prediction

Integration of multi-omics and pharmacological data can help researchers understand the impact of drugs on dynamic biological systems. Network-based approaches to such integration explore the interaction of different cellular components and drugs. However, with ever-increasing amounts of data, processing these high-dimensional biological networks requires powerful tools. We investigate whether network embeddings can address this problem by providing an effective method for dimensionality reduction in drug-related networks. A neural network-based embedding method is employed to encode protein-protein, protein-disease, drug-drug and drug-disease networks for the prediction of novel drug-target interactions. We found that drug-target interaction prediction using embeddings of heterogeneous networks as input features performs comparably to state-of-the-art methods, exhibiting an area under the ROC curve of 84%, outperforming methods such as BLM-NII and NetLapRLS, and coming very close to the best performing network methods such as HNM, CMF and DTINet. These encouraging results suggest that further investigation of this approach is warranted.