Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning

A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.

Automated summary

A drug-drug interaction (DDI) refers to the association between drugs where one drug's pharmacological effects are influenced by another drug. This study proposes a novel method, called DDI-IS-SL, to predict DDIs using integrated similarity and semi-supervised learning. DDI-IS-SL combines drug chemical, biological, and phenotype data to calculate the feature similarity of drugs. It also uses a semi-supervised learning method to calculate the interaction possibility scores of drug-drug pairs. DDI-IS-SL demonstrates better prediction performance and shorter computation time compared to other methods, and its performance is further supported by case studies.