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Abstract Lipid nanoparticles (LNPs) enable RNA medicines, yet ionizable-lipid discovery remains largely empirical, particularly when in vitro activity must be translated into functional hepatic delivery in vivo. Here we establish an interpretable discovery framework that combines modular degradable lipid chemistry, high-throughput phenotyping, machine learning, virtual library expansion, and independent in vivo validation to define a learnable ionizable-lipid design space.
Loading metrics Open Access Peer-reviewed Research Article Citation: Hu Z, Hu K, Hasan MMK (2024) A bidirectional reversible and multilevel location privacy protection method based on attribute encryption. PLoS ONE 19(9): e0309990.
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