The article discusses the growing role of machine learning (ML) and artificial intelligence (AI) in drug delivery, emphasizing their potential to revolutionize the field. Traditional drug delivery methods rely on trial-and-error experimentation and rational design, but as materials and drugs become more complex, the "Curse of Dimensionality" poses challenges in understanding structure-function relationships. High-throughput screening has been used to overcome this, but it is inefficient. ML and AI offer powerful tools to model complex data and uncover quantitative structure-function relationships, enabling more efficient drug delivery systems. The author, Adam J. Gormley, highlights the importance of integrating ML into drug delivery research, noting that while AI/ML should not replace mechanistic understanding, they can serve as a valuable tool for navigating complex parameter spaces. He discusses the challenges of data collection, quality, and representation in drug delivery, emphasizing the need for standardized data practices and repositories. The article also addresses the importance of explainable AI to ensure transparency and trust in AI-driven drug delivery designs. The author advocates for the adoption of ML in drug delivery, noting that while there is excitement around AI/ML, it is important to approach it with caution and ensure that traditional experimental methods are not abandoned. He also discusses the potential of autonomous labs and active learning to improve drug delivery research through iterative experimentation and efficient model training. The article concludes with a call to action for the drug delivery community to embrace AI/ML, emphasizing the need for education, training, and standardization in data handling and representation. The author believes that with the right approach, AI/ML can significantly enhance drug delivery research and lead to groundbreaking advancements in the field.The article discusses the growing role of machine learning (ML) and artificial intelligence (AI) in drug delivery, emphasizing their potential to revolutionize the field. Traditional drug delivery methods rely on trial-and-error experimentation and rational design, but as materials and drugs become more complex, the "Curse of Dimensionality" poses challenges in understanding structure-function relationships. High-throughput screening has been used to overcome this, but it is inefficient. ML and AI offer powerful tools to model complex data and uncover quantitative structure-function relationships, enabling more efficient drug delivery systems. The author, Adam J. Gormley, highlights the importance of integrating ML into drug delivery research, noting that while AI/ML should not replace mechanistic understanding, they can serve as a valuable tool for navigating complex parameter spaces. He discusses the challenges of data collection, quality, and representation in drug delivery, emphasizing the need for standardized data practices and repositories. The article also addresses the importance of explainable AI to ensure transparency and trust in AI-driven drug delivery designs. The author advocates for the adoption of ML in drug delivery, noting that while there is excitement around AI/ML, it is important to approach it with caution and ensure that traditional experimental methods are not abandoned. He also discusses the potential of autonomous labs and active learning to improve drug delivery research through iterative experimentation and efficient model training. The article concludes with a call to action for the drug delivery community to embrace AI/ML, emphasizing the need for education, training, and standardization in data handling and representation. The author believes that with the right approach, AI/ML can significantly enhance drug delivery research and lead to groundbreaking advancements in the field.