Drug–Drug Interaction Prediction: Paradigm Shifts, Key Bottlenecks, and Future Directions

Polypharmacy has become a routine practice in modern medicine, yet the risks of drug–drug interactions (DDIs) remain a critical challenge for patient safety. Given the vast number of possible drug combinations and the impracticality of exhaustive clinical testing, computational approaches have become indispensable for DDI prediction. Over the past 15 years, the field has shifted from handcrafted, similarity-based models to deep learning and graph neural networks (GNNs). Prediction tasks have also expanded from binary classification to multi-class, multi-label, cold-start, and higher-order settings. These reflect an emerging paradigm in both methodology and scope. Yet critical bottlenecks remain. Data sparsity, unreliable negatives, class imbalance, and source heterogeneity undermine robustness; models still struggle with generalization to unseen drugs, with mechanistic interpretability, and with capturing asymmetric or higher-order interactions. These limitations continue to impede translation into clinical and regulatory practice. In this Advanced Review, we critically assess methodological evolution, benchmark datasets, and emerging paradigms, including GNNs, large language models (including multimodal extensions), and generative AI, and examine their promises and limitations. We argue that next-generation progress hinges on unified multimodal and mechanism-aware frameworks, strategies for robust learning under cold-start and long-tail scenarios, and the integration of causal inference with generative approaches to enhance interpretability. By synthesizing past advances with forward-looking perspectives, this review outlines strategic pathways for accelerating the transition of DDI prediction toward intelligent, interpretable, and clinically actionable solutions. This article is categorized under: