Predicting Drug-Drug Interactions Using Deep Neural Network

Abstract

Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Recently, deep neural network (DNN) models have achieved great success in many applications, including predicting pharmacological properties of drugs and drug repurposing. In this study, we generated features produced by SMILES (simplified molecular-input line-entry system) codes for more than 5,000 drugs downloaded from DrugBank. We built a deep neural network model to predict 80 DDI types using the features. We reached an overall accuracy and AUC (area under the curve) of receiver operating characteristic of 93.2% and 94.2% of the test data set and 94.9% and 95.6% of the validation data set, respectively. The trained model was applied to predict the DDI types of 13,155,885 drug-drug pairs combined by 5,130 drugs. The prediction results were applied to analyze the drugs currently used for treating inflammatory bowel disease (IBD). The potential drug combinations for treating IBD were discussed. These results can provide important insights on drug repurposing and guidelines during drug development.