Lipid nanoparticles (LNPs) have emerged as significant drug delivery carriers due to their excellent biocompatibility and high drug-loading capacity. Traditional LNP development, however, relies heavily on costly and time-consuming experimental approaches. Recently, artificial intelligence (AI), particularly machine learning, has advanced nanomedicine development by enabling more efficient data-driven formulation strategies. By leveraging high-dimensional experimental datasets, AI can optimize formulations as well as predict physicochemical properties, in vitro and in vivo behaviors, and therapeutic efficacy, thereby significantly reducing development timelines. This review highlights recent advances in AI technologies and their potential for reshaping the development process of LNPs with high delivery efficiency. Firstly, this review outlines the core physicochemical properties widely used in AI-assisted LNP development. Next, AI tools, techniques, and the rationale behind selecting specific techniques for NP development are elucidated. Subsequently, the critical role of AI in LNP development is also discussed, highlighting its contribution to formulation design, property prediction, biological analysis, and efficacy evaluation. Finally, current challenges and the future opportunities for AI in advancing precision drug delivery are discussed, providing a foundation for building the rational and efficient LNP design to support personalized precision therapy.
Huang et al. (Mon,) studied this question.