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Designing of Information Model for Prediction of Drug-drug Interactions based on Calculation of Target and Therapeutic Similarity

Understanding and predicting the drug–drug interactions is an important in both drug development and clinical application, especially for co-administered medications. We propose a new information model for drug–drug interaction analysis, based on the common biological targets and therapeutic similarity. Based on the data from DrugBank, our model calculates target similarity and therapeutic similarity features. To predict the possible drugdrug interactions it uses a semi-supervised approach, defined in two steps: adding the missing labels using the clustering algorithm K-Means, and then, executing a classification with a supervised learning model Support Vector Machine. Proposed model is tested for the known data set and had shown the high classification rate, with the AUC=98.5+-0.05.