AI Guided Nanocarrier Design and Optimization in Drug Delivery

The intersection of nanotechnology and artificial intelligence (AI) has revolutionized drug delivery system development, particularly in nanocarrier design and optimization. Nanocarriers, liposomes, micelles, dendrimers, and other nanoparticles have been shown to improve pharmacokinetics, minimize systemic toxicity, and target drugs to a site. The usual trial-and-error methods employed in developing nanocarriers are becoming increasingly ineffective as these methods are costly and inefficient and cannot consider the multifactorial complexity of biological systems. AI, including machine learning (ML), deep learning (DL), and data-driven predictive modelling, provides an effective solution to these shortfalls. Aided by artificial intelligence strategies, the best formulation and protein interaction predictions, modelling of release kinetics, and designing of new nanostructures with specific therapeutic goals can all be quickly identified. Alternative large ML models include support vector machines (SVM), random forests (RF), neural networks, and generative algorithms, which can be used to investigate nonlinear relationships among multiple variables that regulate nanocarrier performance. This chapter presents an extensive and technical synopsis of the development of AI-mediated nanocarriers, discussing existing applications, research methodological frameworks, and the future of precision nanomedicine. By incorporating artificial intelligence in the design of nanocarriers, we offer a pathway for transitioning the field of drug delivery into a period of logic-based smart engineering that will make a tremendous difference in the effectiveness, safety, and individual tailoring of therapeutic nanocarriers.