Artificial Intelligence-Enabled Design and Optimization of Lipid Nanoparticle Drug Delivery Systems: Recent Advances and Future Perspectives

Lipid nanoparticle (LNP)-based drug delivery systems have emerged as a transformative platform in modern nanomedicine, offering efficient delivery of small molecules, proteins, peptides, and nucleic acid therapeutics. The clinical success of LNP-enabled messenger RNA (mRNA) vaccines has accelerated research into the development of advanced lipid formulations with improved stability, encapsulation efficiency, targeted delivery, and therapeutic efficacy. However, conventional formulation development relies heavily on trial-and-error experimentation, making the optimization process labor-intensive, time-consuming, and expensive due to the complex interactions among lipid composition, physicochemical properties, manufacturing parameters, and biological performance. Artificial Intelligence (AI): encompassing machine learning (ML): deep learning (DL): reinforcement learning (RL): and other data-driven computational approaches, has emerged as a powerful tool for overcoming these challenges. AI enables rapid analysis of multidimensional datasets, prediction of critical quality attributes, optimization of formulation parameters, virtual screening of lipid libraries, and acceleration of formulation development while minimizing experimental burden. Furthermore, AI-driven models facilitate prediction of particle size, zeta potential, encapsulation efficiency, drug release behavior, biodistribution, and in vivo therapeutic performance, thereby supporting Quality by Design (QbD) principles and precision nanomedicine. Recent advances in explainable AI, generative AI, digital twins, robotics-assisted laboratories, and autonomous experimentation have further expanded the capabilities of intelligent pharmaceutical development. Despite these promising developments, challenges related to data availability, model interpretability, regulatory acceptance, and standardization remain significant barriers to widespread implementation. This review provides a comprehensive overview of AI-enabled design and optimization of lipid nanoparticle drug delivery systems, highlighting recent technological advances, current applications, existing limitations, and future perspectives. The integration of AI with nanotechnology is expected to revolutionize pharmaceutical research by enabling faster, more cost-effective, and highly personalized drug delivery systems, ultimately improving therapeutic outcomes and accelerating the translation of innovative nanomedicines from laboratory research to clinical practice.