Drug–Drug Interaction Prediction Using SMOTE and Gray Wolf Optimizer: Comparative Analysis of Machine Learning and Deep Learning Models

Drug–drug interaction (DDI) prediction plays a critical role in optimizing therapeutic outcomes and enhancing patient safety. DDIs pose challenges in drug discovery, often leading to adverse effects, reduced efficacy, or unexpected outcomes. AI in DDIs acts as an effective tool for analyzing and predicting DDIs which introduced efficient computational approaches to DDI prediction. This paper aims to provide a comprehensive understanding of how ML and DL models perform in DDI prediction. This paper presents a comparative analysis based on key performance metrics such as accuracy, precision, recall and F-score for different ML and DL Models. We used Synthetic Minority Oversampling Technique (SMOTE) and the Gray Wolf Optimizer (GWO) which achieved the best accuracy of 95.42%. Combining the GWO with SMOTE addresses both optimization and data imbalance challenges in DDI prediction. Effectively, SMOTE addresses the class imbalance issue that leads to poor performance. SMOTE improves model performance by generating synthetic examples of the minority class rather than merely duplicating existing ones. This helps create a balanced dataset, enabling the model to learn the decision boundaries more accurately. SMOTE reduces the risk of overfitting. The GWO serves as a metaheuristic optimization framework that enhances model performance by guiding optimal feature selection subsets. This optimization process improves the model’s ability to capture complex, non-linear interaction patterns, leading to enhanced results. In our result, we achieve an accuracy of over 94% which helps in drug safety and therapeutic decision-making in health informatics.