使用机器学习探索天然药物的血清可访问成分规则的新范式,并开发和验证直接预测模型
Qi Yang1, Lihao Yao1, Zhiyang Chen2
1Faculty of Pharmacy, Guangxi University of Chinese Medicine, Nanning 530200, China; Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning 530200, China.
International journal of pharmaceutics
|January 18, 2025
概括
对于血清可获取的天然药物,提出了一项新的指导方针,即自然医学规则5 (NMRO5). 在预测药物可访问性和有效性方面,NMRO5的表现优于Lipinski的五项规则 (RO5).
科学领域:
- 药学研究 药学研究
- 药用化学 医学化学
- 计算机化药物发现技术
背景情况:
- 利宾斯基的五项规则 (RO5) 是药物开发的基石,指导了口服生物可用药物候选者的选择.
- 然而,越来越多的批准药物偏离了RO5,需要更新指南,特别是对于自然医学成分.
研究的目的:
- 建立一套新的预测规则,称为自然医学规则5 (NMRO5),特别是来自天然药物的血清可访问成分.
- 将NMRO5与RO5在预测血清可访问性的有效性进行比较,并为此特性开发直接预测模型.
主要方法:
- 一个全面的文献综述确定了血清可访问的自然医学成分.
- 机器学习技术被用来分析分子特征,并区分血清可访问和非血清可访问的化合物.
- 根据关键的分子特性定义了NMRO5规则,并使用体内实验和分子修改开发和验证了预测模型.
主要成果:
- NMRO5的特点是TPSA,MaxEState和PEOE VSA1值较高,LogP和MinEState值较低,这表明天然药成分的血保留更好.
- 基于NMRO5的预测模型与基于RO5的模型相比,表现优越.
- 血清可访问性的直接预测模型实现了0.7257的准确性,F1得分为0.7223,AUC为0.7553.
结论:
- 与RO5相比,NMRO5提供了一个比RO5更可靠的框架来评估天然药物成分的血清可访问性.
- 开发的预测模型准确地识别出血清可访问的化合物,有助于发现和开发有效的基于天然产品的疗法.
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