AI-OPTIMIZED DESIGN OF LIPID NANOPARTICLES

Lipid nanoparticles (LNPs) have become the leading non-viral delivery platform for messenger RNA (mRNA) and other nucleic acid therapeutics, as demonstrated by their central role in mRNA-based vaccines and emerging gene-editing medicines. Historically, LNP formulation has relied on iterative, trial-and-error chemistry that is costly, time-consuming, and difficult to generalize across cargos and target tissues. Artificial intelligence (AI) and machine learning (ML) are transforming this paradigm by enabling data-driven lipid discovery, predictive modelling of formulation properties, generative design of novel ionizable lipids, and closed-loop optimization through high-throughput experimentation. This chapter surveys the state of the art in AI-optimized LNP design, covering structure–activity relationship (SAR) modelling, machine learning approaches for predicting particle size, surface charge, and stability, generative architectures for de novo lipid design, and the integration of robotics and active learning for autonomous formulation development. Strengths, limitations, and translational and regulatory considerations are critically discussed, and future directions toward fully autonomous nanomedicine laboratories are outlined.