Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery
Wei Wang, Kepan Chen, Ting Jiang, Yi‐Yang Wu, Zhenhua Wu, Hang Ying, Hang Yu, Jing Lu, Jinzhong Lin, Defang Ouyang
Abstract
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships. Ionizable lipid optimization is essential for mRNA therapy via lipid nanoparticle, but experimental screening is investment-intensive. Here, authors developed AI models achieving the rational design of lipid molecules and fast high-throughput screening.