一个QSPR分析和各种基于度的拓指数的曲线回归模型:昆类抗生素
B Kirana1,2, M C Shanmukha3,2, A Usha4
1Department of Mathematics, KVG College of Engineering, Sullia, 574327, India.
Heliyon
|July 8, 2024
概括
本研究探讨了使用定量结构-性质关系 (QSPR) 分析和拓指数的类抗生素. 开发了新的曲线回归模型来预测药物特性,增强QSPR/QSAR应用.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 在QSPR/QSAR中,拓索引对于数值定义化学化合物至关重要.
- 奎诺隆是重要的合成抗生素,用于治疗细菌感染,包括尿路感染.
- 之前的QSPR/QSAR研究通常依赖于线性回归模型.
研究的目的:
- 使用QSPR分析计算各种类抗生素的拓指数.
- 开发和评估曲线回归模型 (线性,二次性,立方体和更高度) 用于预测物理化学性质.
- 引入和应用一种新的概念,用于找到最小的作为预测指数.
主要方法:
- 对诺隆抗生素的拓指数的计算.
- 应用 QSPR 分析以将拓指数与物理化学性质相关联.
- 开发和图形表示线性,二次性,立方性,四度和五度曲线线性回归模型.
- 使用一种新的最小测量方法来进行预测指数评估.
主要成果:
- 已建立的曲线回归模型用于拓指数和诺的物理化学性质.
- 图形展示了不同度回归模型之间的变化.
- 确定最小的作为预测QSAR/QSPR分析的重要措施.
结论:
- 与传统的线性模型相比,曲线回归模型为QSPR/QSAR分析提供了更全面的方法.
- 新的最小概念为预测化学化合物特性提供了有价值的工具.
- 这项研究增强了类抗生素研究中的理解和预测能力.
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