A systematic review of molecular structures, knowledge graphs, and cold-start scenario in drug-drug interaction...

Drugs are essential chemical substances used to treat, prevent, or diagnose diseases. However, the co-administration or simultaneous use of multiple drugs can lead to complex interactions known as Drug-Drug Interactions (DDIs). Adverse DDIs are a major concern in the pharmaceutical industry, posing significant health risks by potentially causing toxicity or negatively impacting therapeutic efficacy. Identifying all DDIs through clinical trials alone is not feasible, making computational approaches crucial for predicting and understanding these interactions. Molecular structures and biomedical entities represented as knowledge graphs (KGs) have been widely used for DDI prediction. This review first examines diverse molecular representations commonly utilized in DDI prediction. It then highlights KG-based approaches, emphasizing their ability to integrate heterogeneous biomedical data and provide comprehensive structural and relational insights into drugs, proteins, and other biological entities. However, predictive models frequently encounter challenges when dealing with drugs with limited interaction data or unknown structures, resulting in a cold-start scenario that negatively impacts model generalization. Consequently, this review also discusses methods to address the cold-start scenario in DDI prediction. Finally, key findings and potential directions for enhancing DDI prediction are presented.