Drug-Drug Interaction Prediction Model Based on Deep Learning Using Drug Information Document Embedding

Sunwoo Jung, Sunyong Yoo

http://doi.org/10.5626/JOK.2024.51.6.503

The use of polypharmacy has emerged as a promising approach for various diseases, including cancer, hypertension, and asthma. However, the use of polypharmacy can result in unexpected interactions, which may lead to adverse drug effects. Therefore, predicting drug-drug interactions (DDI) is essential for safe medication practices. In this study, we propose a drug-drug interaction prediction model based on deep learning using document embedding to represent the drug. We generate documents about drug information by combining DrugBank data, which includes drug descriptions, indications, mechanisms of action, pharmacodynamics, and toxicity. Then we use Doc2Vec and BioSentVec language models to generate drug representation vectors from the drug information documents. The two drug vectors are paired and input into the deep learning-based prediction model, which outputs the likelihood of interaction between the two drugs. Our goal is to construct the optimal model for predicting drug-drug interactions by comparing the performance under various conditions, including language embedding model performance and adjustments for data imbalance. We expect the proposed model to be utilized for the advanced prediction of drug interactions during the drug prescription process and the clinical stages of new drug development.