Paper Title
Drug – Drug Interaction Checker System

Abstract
Drug–drug interactions (DDIs) can cause side effects and reduce the effectiveness of treatments. Traditional ways to find DDIs, like clinical trials or post-market studies, are slow and expensive. AI methods can help, but many struggle to understand complex drug names. In this work, a simple and clear pipeline is presented that uses BioBERT to create embeddings for drugs and Logistic Regression to predict interactions. By combining powerful biomedical embeddings with a simple classifier, the system performs well on a standard DDI dataset. This shows that transformer-based models with traditional machine learning can predict DDIs effectively and efficiently. Keywords - Drug–Drug Interaction, BioBERT, Logistic Regression, Drug Safety, Machine Learning