加速基于西洛的可电离性脂质设计LNP与数据效率高的科尔摩戈罗夫-阿诺德网络
Yujing Zhao1,2, Juntao Wang2, Yuxin Song2
1MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, China.
Journal of chemical information and modeling
|February 4, 2026
概括
这项研究引入了一种使用科尔莫戈罗夫-阿诺德网络 (KAN) 的新型机器学习框架,以加快用于mRNA疫苗的电离性脂质的设计. 该方法有效地识别出高性能脂质候选者,提高了输送效率.
科学领域:
- 生物材料科学 生物材料科学
- 计算化学的计算化学
- 药物运输 药物运输 药物运输
背景情况:
- 电离性脂质对于脂质纳米粒子 (LNP) 的有效性至关重要,特别是在mRNA疫苗中.
- 开发新的电离性脂质是具有挑战性的,因为复杂的结构-属性关系和有限的数据.
研究的目的:
- 开发一个小型数据驱动的机器学习 (ML) 框架,以加速发现基于素的新型电离性脂质.
- 率先使用科尔莫戈罗夫-阿诺德网络 (KANs) 预测电离性脂质性能.
主要方法:
- 采用一个小数据驱动的框架,利用Kolmogorov-Arnold网络 (KANs),一种符号回归ML方法.
- 集成的KAN与虚拟选和雨抽样模拟用于候选人识别.
- 使用分子动力学模拟来评估结合内体膜的亲缘关系的验证结果.
主要成果:
- 该KAN模型仅使用36个训练样本实现了mRNA传递效率的高预测精度 (Qcv2 = 0.710).
- KAN模型的性能优于传统的ML模型,并提供了明确的数学公式.
- 确定了三种候选脂质,其中最优的脂质在结合内体膜的亲和力上增加了187%.
结论:
- 拟议的框架为ML引导的电离性脂质设计提供了一个数据效率高的范式.
- 成功地将符号回归与分子动力学验证用于LNP治疗方法.
- 为设计下一代基于LNP的治疗方法铺平了道路,包括mRNA疫苗.
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