A Neighborhood-Based Global Network Model to Predict Drug-Target Interactions

The detection of drug-target interactions (DTIs) plays an important role in drug discovery and development, making DTI prediction urgent to be solved. Existing computational methods usually utilize drug similarity, target similarity and DTI information to make prediction, providing the convenience of fast time and low cost. However, they usually learn features for drugs and targets separately, lacking of a global consideration. In this study, we proposed a novel neighborhood-based global network model, named as NGN, to accurately predict DTIs from the global perspective. We designed a distance constraint for features of all entities (drugs and targets) in the latent space to ensure the close distance between adjacent entities, and defined a global probability matrix to compute the predicted DTI scores on our constructed neighborhood-based global network. Results showed that NGN obtained advantageous performance compared with other state-of-the-art methods, especially surpassing them by 4.2-9.1 percent on AUPR values in the biggest dataset. Furthermore, several novel high-ranked DTIs were successfully predicted with confirmations by public sources, demonstrating the effectiveness of our method.