Enhancing Drug-Drug Interaction Prediction with Auxiliary Drug Similarity Estimation using Convolutional Neural Networks

Abstract

In the last few years, a rapid development of artificial intelligence has revealed its possible applications in many tasks. One of those areas where artificial intelligence showed great promise was predicting drug-drug interactions, which had an enormous amount of data to be interpreted through. Drug-drug interactions are caused by interactions between drugs; it can decrease the effectiveness, lead to serious emergencies of the patient, or have synergies that increase the effectiveness of both. The previous knowledge-based drug-drug interaction prediction methods were costly and time consuming. Thus, a model that can predict these interactions is highly demanded for patients’ safety, furthermore, providing insights to scientists in medical fields. In this research, I propose a similarity aware drug-drug interaction predicting model. The proposed method consists of three modules: a drug feature extractor, a drug similarity estimator, and a drug-drug interaction predictor. The trained network is designed to be drug similarity-aware, which leads to significantly improved accuracy compared to the naive models. By incorporating drug similarity information, the model can provide more precise and reliable predictions for drug-drug interactions. The experimental result of the proposed deep-learning model achieved state-of-the-art performance compared to other previous methods.