0
0This paper provides a comprehensive analysis of machine learning (ML) and artificial intelligence (AI) methodologies used to predict drug-drug interactions (DDIs). It explains that traditional experimental methods for identifying these interactions are often too slow and expensive to keep pace with modern pharmacology. To solve this, researchers utilize biomedical databases and computational models like graph neural networks and multi-task learning to identify how combined medications might cause adverse effects. The text details a standardized workflow involving data acquisition, model construction, and experimental validation to ensure clinical reliability. While these technological advances offer improved scalability and accuracy, the authors emphasize that challenges regarding model interpretability and real-world integration remain. Ultimately, the work advocates for data-driven strategies to enhance patient safety and optimize therapeutic decision-making.
References:
Lu Y, Chen J, Fan N, et al. Machine learning models for drug-drug interaction prediction from computational discovery to clinical application[J]. npj Digital Medicine, 2026.
