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利用整体机器学习模型 (XGBoost和随机森林) 和遗传算法来预测导致治疗药物的脂质体陷的因素

Fatemeh Khodadadi¹, Fatemeh Taghizadeh¹, Ali Hashemi Baghi²

¹Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran. a_haeri@sbmu.ac.ir.

|September 12, 2025

概括

机器学习模型通过分析药物溶解性和大小等因素来预测脂质体捕获效率. 使用遗传算法进行优化可提高药物输送系统开发的模型准确性.

科学领域:

制药科学制药科学
计算化学的计算化学
生物技术是生物技术.

背景情况:

脂质体是安全的,可生物降解的药物输送系统,对于向治疗至关重要.
在应用之前,描述脂质体,特别是捕获效率 (EE%),至关重要.
人工智能 (AI) 和机器学习 (ML) 为分析药物输送中的复杂数据集提供了强大的工具.

研究的目的:

应用整体机器学习模型来预测和识别影响脂质体捕获效率 (EE%) 的关键因素.
评估与货物和载体相关性质对各种治疗药物的EE%的影响.
利用可解释的人工智能来解释模型结果和理解变量的重要性.

主要方法:

采用了一种整体机器学习方法,将随机森林和XGBoost算法与遗传算法结合起来.
用500个数据点的数据集来训练和验证模型.
使用可解释的人工智能技术来解释结果和变量排名.

主要成果:

对于所有药物来说,水溶性,大小,胆固醇与脂分子比率以及药物与脂质分子比率是影响EE%的最有影响力的因素.
对于logP> 1的药物,关键因素是水溶性,logP,相变温度 (Tm) 和大小.
对于logP<1的药物,影响因素包括水溶性,胆固醇与脂的摩尔比率,logP和大小.

结论:

机器学习,特别是用遗传算法优化的集合方法,有效地预测脂质组EE%.
关键的物理化学性质显著影响药物被困在脂质体中,根据药物的脂友性 (logP) 变化.
这种方法有助于优化脂质体配方参数,以改善药物输送.

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