Machine learning models for drug-drug interaction prediction from computational discovery to clinical application

Drug-drug interaction (DDI) poses a major challenge in clinical pharmacology, often compromising therapeutic efficacy or causing serious adverse events. Traditional detection methods, heavily dependent on experimental assays and expert knowledge, are constrained by high costs and limited scalability. This work explores emerging machine learning (ML)-based strategies for predicting DDIs by leveraging the rapidly expanding biomedical data landscape. Recent advances in deep learning architectures, graph neural networks and sophisticated feature engineering have markedly improved predictive performance, offering scalable and data-efficient alternatives to conventional approaches. We further highlight real-world clinical applications where ML-based models have enhanced drug safety monitoring and informed therapeutic decision-making. Finally, we discuss critical challenges like model interpretability, generalizability and integration with clinical workflows, and outline future directions toward building robust, explainable and clinically actionable DDI prediction systems. This work provides a comprehensive perspective on how AI-driven methodologies are reshaping pharmacovigilance and precision therapeutics.