Artificial intelligence in the rational design of lipid nanoparticles for mRNA therapeutics
TL;DR
This review surveys how artificial intelligence is converting this nonlinear design space from empirical iteration into data-efficient, multi-objective optimization and outlines three priorities for translation: standardized data and metadata, mechanistic endpoints for escape and immunogenicity, and CMC-aware optimization.
Abstract
Lipid nanoparticles (LNPs) have made RNA therapeutics clinically viable, yet delivery remains the dominant constraint on efficacy and safety as applications expand beyond the liver. Performance is emergent: it reflects coupled choices in ionizable-lipid chemistry, multi-component formulation, and process history, and is further reshaped by biological interfaces including protein corona remodeling, tissue transport barriers, endocytic trafficking, and low-probability endosomal escape. This review surveys how artificial intelligence is converting this nonlinear design space from empirical iteration into data-efficient, multi-objective optimization. We highlight (i) structure-activity learning and synthesis-aware generative modeling for ionizable lipid discovery; (ii) formulation- and process-conditioned architectures that treat LNPs as composite, process-defined materials; and (iii) pooled in vivo barcoding and single-cell readouts that enable prediction and tuning of organ and cell-type tropism. We conclude by outlining three priorities for translation: standardized data and metadata, mechanistic endpoints for escape and immunogenicity, and CMC-aware optimization. Progress on these fronts will be necessary for potent, safe, and manufacturable mRNA-LNP medicines.