使用基于图表的基于度的拓指数和回归模型对药物的QSAR分析
Zeeshan Saleem Mufti1,2, Aqsa Kabeer1, Abdulrahman A Almehizia3
1Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Scientific reports
|January 6, 2026
概括
拓指数有效地预测药物的物理化学性质. 图形理论描述符,特别是当用二次回归分析时,在QSPR建模中提供了一种强大的方法来理解药物结构与属性关系.
科学领域:
- 计算化学计算化学
- 药用化学 医学化学
- 化学信息学 化学信息学
背景情况:
- 了解药物结构与特性关系对于药物开发至关重要.
- 分子网络分析和拓指数提供了量化分子架构的系统方法.
- 物理化学性质显著影响药物的疗效和行为.
研究的目的:
- 调查9种药物的拓指数和物理化学性质之间的相关性.
- 在定量结构与属性关系 (QSPR) 建模中评估十个不同的拓指数的预测能力.
- 为了比较线性,对数和二次回归模型的有效性.
主要方法:
- 计算了10个拓指数 (例如,ABC,RI,GA,SC,扎格勒布指数,HZ) 对9种选定的药物.
- 评估了这些药物的八种基本物理化学特性.
- 采用线性,对数和二次回归分析来建模结构-属性相关性.
主要成果:
- 在选择的拓指数和药物物理化学性质之间观察到强烈的相关性.
- 与线性和对数模型相比,二次回归显示出更高的预测性能.
- 该研究证实了图形理论描述符在药物QSPR建模中的实用性.
结论:
- 拓指数,特别是使用二次回归等非线性建模的拓指数,对于药物结构与性质相关性具有很高的预测潜力.
- 图形理论描述符是解释和预测药物行为的宝贵工具.
- 这种方法有助于合理设计和开发新的治疗药物.
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