Predicting Drug–Target Interaction Networks of Human Diseases Based on Multiple Feature Information

Abstract

Drug-target interaction is crucial in the drug design process. Predicting the drug-target interaction networks of important human diseases can provide valuable clues for the characterization of the mechanism of action of diseases. A new graph-based semisupervised learning (GBSSL) method is proposed to predict the drug-target interaction networks involved in 13 types of diseases. According to the method, each drug-target pair is initially described with different biological features including sequence, structure, function and network topology information. Then, the optimal feature selection procedures based on the relief and minimum redundancy maximum relevance are executed, respectively. Finally, unknown drug-target interactions can be predicted by the GBSSL method effectively. The proposed method can effectively predict drug-target interactions (with a receiver operating characteristic score of 94.8% and a precision-recall score of 76.5%). Compared with the existing methods, the GBSSL method provides an efficient means of generating optimal features obtained from the combination of multiple sources of feature information.