Lipid nanoparticles (LNPs) have emerged as the leading delivery platform for nucleic acid therapeutics, with clinical success demonstrated in mRNA vaccines and therapeutic applications. However, rational design of LNP remains challenging due to the complex relationships among LNP composition, physicochemical properties, and biological performance, as well as the scattered experimental data across the literature. Here, we present LNP Atlas, a comprehensive dataset containing LNP formulations extracted from peer-reviewed publications. We developed an artificial intelligence-assisted data extraction workflow, followed by automated standardization pipelines implemented in Python to ensure data consistency and quality. The dataset contains lipid types and compositions, including molar ratios and SMILES codes, physicochemical properties (particle size, polydispersity index, zeta potential), synthesis parameters, and bioactivity profiles. This resource is expected to support the research community by facilitating data-driven insights into lipid nanoparticle formulation and accelerating the development of effective nucleic acid delivery systems.
Song et al. (Sat,) studied this question.
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