A survey of deep learning methods for drug-drug interaction prediction

Xu Guo, Siqi Chen, Jianbo Qiao, Leyi Wei

Abstract: Drug-Drug Interaction (DDI) prediction plays a critical role in ensuring clinical medication safety and optimizing therapeutic regimens, while also representing a significant challenge in the drug development process. With the rapid advancement of artificial intelligence technologies, computational methods based on machine learning and deep learning have emerged as the mainstream paradigm in DDI research. In this survey, we systematically review the latest research progress and establish a clear methodological taxonomy that traces the evolution from classical feature engineering to modern deep learning architectures. Beyond detailing foundational molecular representations, we provide a comprehensive overview of DDI prediction techniques, encompassing sequence-based models, graph-based models, Transformers, and Graph Transformers. Our analysis culminates in a dedicated discussion of emerging advanced strategy paradigms, such as multimodal fusion and specialized pre-training and fine-tuning schemes. Furthermore, we synthesize current challenges with contemporary solutions and discuss their practical implications for clinical decision support, providing a forward-looking perspective for the continued development of DDI prediction models.

External IDs:doi:10.70401/cbm.2025.0005