计算生物学
纳米技术
计算机科学
化学
生化工程
输送系统
钥匙(锁)
信使核糖核酸
过程(计算)
纳米颗粒
蛋白质稳定性
设计策略
作者
Kexin Su,Junjie Qiu,Tengfei Xu,Shuai Liu
标识
DOI:10.1016/j.apsb.2025.11.029
摘要
Lipid nanoparticles (LNPs) hold significant potential for mRNA-based therapeutics, as evidenced by their successful use in SARS-CoV-2 mRNA vaccines. LNPs effectively protect and transport mRNA to target sites, thereby ensuring its stability and efficient transfection. Despite the progress, some challenges remain in the development of mRNA-LNP delivery systems, such as limited targeting specificity, the complexity of formulations, and the time-consuming and high-throughput screening process. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges, accelerating the design and optimization process of LNPs. AI-guided approaches can improve the efficiency of lipid structure and formulation screening by rapidly identifying key design parameters and employing predictive modeling to optimize LNP properties. The combination of AI and LNP technology offers significant advantages, including enabling the design of more personalized and precise delivery systems, streamlining the development process, and reducing the cost. This review discusses recent advancements in AI-guided mRNA-LNP delivery systems and highlights their potential to revolutionize mRNA therapeutics.
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