Multi-feature machine learning for enhanced drug–drug interaction prediction

Drug–drug interactions are a major concern in healthcare, as concurrent drug use can cause severe adverse effects. Existing machine learning methods often neglect data imbalance and DDI directionality, limiting clinical reliability. To overcome these issues, we employed GPT-4o Large Language Model to convert free-text DDI descriptions into structured triplets for directionality analysis and applied SMOTE to alleviate class imbalance. Using four key drug features (molecular fingerprints, enzymes, pathways, targets), our Deep Neural Networks (DNN) achieved 88.9% accuracy and showed an average AUPR gain of 0.68 for minority classes attributable to SMOTE. By applying attention-based feature importance analysis, we demonstrated that the most influential feature in the DNN model was supported by pharmacological evidence. These results demonstrate the effectiveness of our framework for accurate and robust DDI prediction. The source code and data are available at https://github.com/FrankFengF/Drug-drug-interaction-prediction-