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通过机器学习,对接,动态和DFT分析来识别NLRP3抑制剂的综合计算方法

Sami I Alzarea¹

¹Department of Pharmacology, College of Pharmacy, Jouf University, Sakaka, 72341, Al-Jouf, Saudi Arabia. samisz@ju.edu.sa.

Scientific reports

|December 30, 2025

概括

机器学习发现了针对神经炎症的NLRP3炎症体的新型植物化学物质. ZINC14583344和PubChem 348,482作为中枢神经系统疾病的选择性NLRP3抑制剂具有前景.

科学领域:

药用化学医学化学
计算机化药物发现技术
神经科学是一个神经科学.

背景情况:

神经炎症是由含有3 (NLRP3) 炎症酶的NLR家族皮林域驱动的,与阿尔茨海默氏症和帕金森氏症等中枢神经系统疾病有关.
目前的治疗策略缺乏有效和选择性的NLRP3抑制剂,原因是选择性,类似药物的特性和安全问题.

研究的目的:

通过基于机器学习的虚拟查方法,识别NLRP3炎症体的新型植物化学抑制剂.
评估针对NLRP3 NACHT域的潜在候选药物的结合亲和力,药物动力学特性和稳定性.

主要方法:

一个随机森林机器学习模型在1,956个活跃化合物和5,476个不活跃化合物上进行了训练,达到0.83.6的AUC.
在MPD3植物化学库的虚拟选中,发现了183种类似药物的候选物,随后进行了分子对接,ADMET分析,分子动力学模拟和DFT计算.
分析了与NLRP3残留物 (Ala228,Arg578,Glu629) 的关键相互作用,并使用MM-GBSA计算了结合的自由能量.

主要成果:

机器学习模型成功地从植物化学库中预测了NLRP3抑制剂.
分子对接确定了PubChem 348,482,ZINC14583344和PubChem 11,027,076具有高结合亲和度 (-10.6至-11.3 kcal/mol).
ZINC14583344通过分子动力学模拟和有利的结合能 (-23.99 kcal/mol) 证明了稳定的相互作用,而DFT分析显示了它的反应性和灵活性. PubChem 348,482 显示出强烈的极性相互作用.

结论:

ZINC14583344和PubChem 348,482代表了开发选择性NLRP3抑制剂的有希望的支架.
这些发现为针对中枢神经系统疾病中神经炎症的新疗法提供了基础.
该研究强调了机器学习和计算方法在加速治疗复杂疾病的药物发现方面的有效性.

关键词:

分子动力学分子动力学在NLRP3炎症酶体中,NLRP3炎症神经炎症是一种神经炎症.植物化学品植物化学品虚拟选是一个虚拟的选.

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