The article by Adam J. Gormley, an Associate Professor of Biomedical Engineering at Rutgers University, discusses the potential of artificial intelligence (AI) and machine learning (ML) in drug delivery. Gormley highlights the challenges of optimizing drug release profiles through rational design, particularly in complex systems with multiple interacting variables. He argues that while high-throughput screening has been inefficient, AI/ML offers a powerful tool to model complex data and establish quantitative structure-function relationships. Gormley emphasizes that AI/ML should complement, rather than replace, traditional mechanistic understanding. He provides examples of how AI/ML can be applied to polymeric delivery systems, including the use of neural networks to model high-dimensional data and explainable AI to interpret model outputs. The article also addresses the importance of data quality, quantity, and standardization, and suggests that active learning and autonomous labs can enhance the efficiency of drug delivery research. Gormley encourages scientists to embrace AI/ML, noting that it will become as common as other analytical tools in the future. He concludes by emphasizing the need for training and education to integrate these technologies effectively into drug delivery research.The article by Adam J. Gormley, an Associate Professor of Biomedical Engineering at Rutgers University, discusses the potential of artificial intelligence (AI) and machine learning (ML) in drug delivery. Gormley highlights the challenges of optimizing drug release profiles through rational design, particularly in complex systems with multiple interacting variables. He argues that while high-throughput screening has been inefficient, AI/ML offers a powerful tool to model complex data and establish quantitative structure-function relationships. Gormley emphasizes that AI/ML should complement, rather than replace, traditional mechanistic understanding. He provides examples of how AI/ML can be applied to polymeric delivery systems, including the use of neural networks to model high-dimensional data and explainable AI to interpret model outputs. The article also addresses the importance of data quality, quantity, and standardization, and suggests that active learning and autonomous labs can enhance the efficiency of drug delivery research. Gormley encourages scientists to embrace AI/ML, noting that it will become as common as other analytical tools in the future. He concludes by emphasizing the need for training and education to integrate these technologies effectively into drug delivery research.