Accelerating Siloxane-Based Ionizable Lipid Design for LNPs with Data-Efficient Kolmogorov–Arnold Networks

Ionizable lipids are fundamental to the efficacy of lipid nanoparticles (LNPs) in pivotal areas including mRNA vaccines. Their development, however, is hindered by intricate structure-property relationships and limited experimental data. To address these challenges, this study proposed a small-data-driven framework that pioneered the use of Kolmogorov-Arnold networks (KANs)─a symbolic regression-based machine learning (ML) approach─to accelerate the discovery of novel siloxane-based ionizable lipids. Using only 36 training samples, the resulting KAN model demonstrated high predictive accuracy for mRNA delivery efficiency (<i>Q</i>_cv² = 0.710), outperforming conventional ML models by an average absolute improvement of 0.627 in cross-validation and yielding explicit mathematical formulas. Combined with virtual screening and umbrella sampling simulations, the framework identified three candidate lipids with superior predicted performance. Molecular dynamics simulations validated that the optimal candidate achieved stronger binding affinity to the endosomal membrane, as evidenced by a 187% reduction (from -1.048 to -3.011 kcal/mol) in the binding free energy minimum compared to the best experimental control. This result aligns with the delivery efficiency predicted by the KAN model. Overall, the proposed framework establishes a data-efficient paradigm for ML-guided ionizable lipid design, bridging symbolic regression with molecular dynamics validation for next-generation LNP therapeutics.