Background: Identification of interaction between drugs and target proteins plays an important
role in discovering new drug candidates. However, through the experimental method to identify
the drug-target interactions remain to be extremely time-consuming, expensive and challenging even
nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget
Methods: In this article, a novel computational model is developed for predicting potential drug-target
interactions under the theory that each drug-target interaction pair can be represented by the structural
properties from drugs and evolutionary information derived from proteins. Specifically, the protein
sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information
of biological evolutionary and the drug molecules are encoded as fingerprint feature vector
which represents the existence of certain functional groups or fragments.
Results: Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are
independently used for establishing predictive models with Rotation Forest (RF) model. The proposed
method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively.
In order to make our method more persuasive, we compared our classifier with the state-of-theart
Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent
Conclusions: Experimental results demonstrate that the proposed method is effective in the prediction
of DTI, and can provide assistance for new drug research and development.