Artificial Intelligence in Predictive Modeling of Drug–Drug Interactions: Advances, Applications, and Future Directions

Drug–drug interactions (DDIs) pose a significant clinical challenge, contributing to adverse drug events (ADEs), hospitalizations, and increased healthcare costs. Conventional DDI detection methods such as in vitro assays, in vivo studies, and post-marketing surveillance—provide valuable mechanistic insights but are limited by high cost, time requirements, and incomplete coverage of possible drug combinations. The rapid expansion of available pharmaceuticals has intensified the need for scalable, accurate, and timely predictive approaches. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools capable of integrating large, heterogeneous datasets spanning chemical structures, pharmacokinetics, pharmacodynamics, electronic health records (EHRs), and biomedical literature to uncover complex interaction patterns that may not be evident through traditional methods. This review provides a comprehensive overview of AI-based predictive modeling techniques for DDI identification, encompassing traditional ML algorithms, deep learning architectures, graph-based models, and natural language processing (NLP) approaches. It examines key data sources, including structured databases (e.g., DrugBank, ChEMBL, KEGG), real-world clinical data, and literature mining, and discusses strategies for data preprocessing, feature engineering, and model evaluation. Special attention is given to challenges such as data sparsity, class imbalance, lack of interpretability, and limited generalizability to novel drug combinations. Emerging solutions—including explainable AI (XAI), transfer learning, multimodal data integration, and federated learning—are highlighted as promising directions to enhance transparency, robustness, and clinical applicability. Applications of AI-driven DDI prediction span clinical decision support systems, drug development pipelines, and pharmacovigilance frameworks, with case studies demonstrating reductions in medication errors and improvements in patient safety. The review also addresses regulatory perspectives, ethical considerations, and integration challenges in clinical workflows. Ultimately, AI-based predictive modeling offers a powerful, adaptive, and scalable approach to mitigating DDI risks, supporting precision medicine, and advancing drug safety across healthcare systems.