Heterogeneous Network Propagation with Optimal Similarity Measure for Drug-Target Associations

Abstract

Identification of drug target proteins is an important process in drug discovery and development. To perform predictions of large-scale drug-target interactions, many computational methods have been proposed using the information of target-target and drug-drug similarities into a network. In this work, we propose an enhanced heterogeneous network model with various biological aspects of target-target and drug-drug similarities to predict drug-target associations. Network propagation was performed through the heterogeneous network to predict new potential targets of a certain drug. Cross-validation and the Area Under the Receiver Operating Characteristic curve (AUROC) were then used to qualify the best model in the drug-target association prediction. The result shows that the model using the similarity measures based on drug-disease associations and target protein sequences significantly outperforms the other models and the original model. Overall, our best model yields an accuracy of 93.5%. In conclusion, the heterogeneous model with optimal similarity measures is a well-suited approach to predict a new target for a certain drug.