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R软件用于植物药理学研究中的QSAR分析

Sanjoy Singh Ningthoujam¹, Rajat Nath², Sibashish Kityania²

¹Government Hindi Teachers' Training College, Imphal, Manipur, India.

Phytochemical analysis : PCA

|July 1, 2023

概括

选择最佳描述符是药物设计中可靠的定量结构-活性关系 (QSAR) 模型的关键. 这项研究表明了有效的描述器选择方法和回归诊断,用于准确的QSAR分析.

科学领域:

计算化学是一种计算化学.
化学信息学化学信息学
生物信息学是一种生物信息学.

背景情况:

定量结构-活性关系 (QSAR) 分析对于药物设计和天然产品研究至关重要.
描述符的扩散需要强大的选择方法来准确的QSAR建模.
鉴定相关的独立变量是具有挑战性的,因为大量的可用描述符.

研究的目的:

展示QSAR研究的各种描述器选择技术,包括Boruta,所有子集回归,ANOVA,AIC,逐步回归和遗传算法.
使用R软件进行全面的回归诊断,以评估模型可靠性.
为研究人员提供可访问的工具来选择描述符和诊断QSAR分析中的错误.

主要方法:

使用的描述符选择方法:博鲁塔方法,所有子集回归,ANOVA,AIC,逐步回归和遗传算法.
采用R软件进行回归诊断,包括正常性,线性,残余组图,PP图,多线性和同类性测试.
开发了一种工作流程,以整合描述器选择和回归诊断用于QSAR模型开发.

主要成果:

博鲁塔方法和遗传算法在选择QSAR模型的相关独立变量方面被证明是优越的.
回归诊断成功地发现并解决了模型错误,提高了QSAR模型的可靠性.
实施的工作流程有效地突出了QSAR研究的各种描述器选择和诊断程序.

结论:

有效的描述器选择和回归诊断对于开发可靠的QSAR模型至关重要.
建议采用Boruta方法和遗传算法,以便在QSAR中进行可靠的变量选择.
这项研究为研究人员进行QSAR分析提供了实用和可适应的框架.

关键词:

在MLR中,MLR是MLR.在QSAR中使用QSAR.R 软件软件软件软件软件描述器描述器是一个描述器.功能选择功能选择回归假设的回归假设.回归诊断的回归诊断方法

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