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Updated: May 27, 2025

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计算机辅助发现协同作用的药物-纳米粒子组合,以增强抗菌活性

Susan Jyakhwo¹, Andrei Dmitrenko¹, Vladimir V Vinogradov¹

¹International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002 Saint Petersburg, Russia.

ACS applied materials & interfaces

|February 17, 2025

概括

这项研究开发了一种机器学习方法,以找到针对耐药细菌的协同抗生素和纳米粒子组合. 金纳米颗粒与氨基醇结合,显示出针对沙门氏菌的显著抗菌活性 typhimurium.

科学领域:

生物技术是生物技术.
传染性疾病传染性疾病
材料科学材料科学材料科学

背景情况:

抗生素耐药性是全球主要的健康威胁,原因是有限的新药发现和不断演变的耐药性机制.
涉及抗生素和纳米颗粒 (NP) 的组合疗法对抗多药耐药 (MDR) 细菌有希望.

研究的目的:

开发一种创新的机器学习 (ML) 强化遗传算法 (GA) 方法,用于识别具有增强抗菌活性的协同药物-NP组合.
为了预测药物-NP组合的最小度 (MC) 和抑制区 (ZOI).

主要方法:

编译数据集以训练CatBoost回归模型来预测MC和ZOI,达到0.86和0.77的R2分数.
采用了ML增强的GA来识别协同作用的抗微生物NP.
通过重现先前的实验发现来验证方法.

主要成果:

该ML-GA方法成功地确定了协同作用的药物-NP组合.
黄金纳米粒子 (Au NPs) 与氨基具有很高的协同作用.
这种组合达到71.74 ng/ml的最小杀菌度 (MBC) 针对沙门氏菌 typhimurium,分数抑制度指数为6.23 × 10−3.

结论:

该研究提出了一种有效的策略,用于发现协同作用的抗生素-纳米粒子组合.
这种方法为对抗耐药病原体提供了一个有前途的方法.
这些发现推动了针对抗生素耐药性感染的向抗菌疗法.

关键词:

抗生素抗生素是一种抗生素.遗传算法是一种遗传算法.机器学习是机器学习.纳米颗粒是一种纳米粒子.协同活动的协同活动.

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