几种统计算法模型在药物构效关系中的研究和应用
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摘要
定量构效关系(QSAR)的目的是架设化学与生物学之间的桥梁,从一系列已知活性的化合物中找出结构和生物学活性之间的定量关系,进而预测新化合物的活性,并指导新药的设计。定量构效关系是重要的现代药学与化学基础理论研究与开发应用领域,其核心内容是考察和分析基本分子结构特征与物化性质或生物活性之间的定量相关关系,已成为有机化学、药物化学、环境化学、计算化学、农药乃至分子生物学、免疫学的研究热点。
分子结构表征是定量构效关系研究的一个关键环节,结构描述子能否反映分子与生物活性相关的结构信息,决定了定量构效关系研究的成败。文中对分子电性距离矢量/分子电性相互作用矢量(MEDV/VMEI)等进行深入的研究。
建模方法与技术是定量构效关系研究的一个重要内容。本文主要对多元线性回归(multiple linear regression, MLR)、人工神经网络(artificial neural network, ANN)和遗传算法(Genetic Algorithms ,GA)等几类统计算法进行了研究,与所研究的结构描述子结合,应用于药物的定量构效关系建模研究。
本文研究工作分为三个部分:
第一部分:算法模型的研究,对回归方法(MLR),人工神经网络模型(ANN),遗传算法(GA)等几种构效关系研究中比较重要的统计方法进行研究;
第二部分:分子结构表征模型的研究,主要对分子距边矢量(MDV)模型、分子电性相互作用矢量(VMEI)模型等分子结构描述方法进行讨论,提出改进和完善的方法;
第三部分:结合所研究的分子结构描述方法和算法模型,根据不同药物体系结构与活性内在的函数关系,采用多种统计方法,建立几类药物体系的结构和活性的定量构效关系,取得了良好的效果。所研究的药物体系包括:两个体系的抗艾滋病药物、三个体系的肽类、三类雌激素类药物、DNA启动子以及几类持久性环境污染物和烷烃体系等。
本论文对QSAR药物设计研究中的建模方法和结构描述方法进行了探讨,应用于药物/化合物的构效关系研究,获得了一定的成绩,所建立的相关模型对药物的活性成分筛选和药物设计具有一定的指导作用,值得深入研究。可望通过进一步研究,为药物分子的筛选及其创新药物设计开辟了一条新的道路。
Quantitative Quantitative Structure-Activity Relationship (QSAR), which investigates the quantitative relationship between the molecular structural parameters and biological activities or dependent functions, is one of the most important fundamental fields in pure and applied chemistry and pharmacy. As an important field for basic research and application of modern chemistry,it has become the hotspot of research on organic chemistry, pharmacological chemistry, environment chemistry, chemometrics, molecular biology and immunology.
Structural description is a key step in the QSAR studies. Whether the structural descriptors can reflect the structural variations determines the success of QSAR studies. In this paper, suah as Molecular electro-negativity distance vectors (MEDV) , Molecular Electronegativity Interaction Vector(VMEI) were further Studied.
The modeling methods and related techniques are also important for the success of QSAR studies. The modeling methods such as multiple linear regression (MLR), Artificial Neural Network, Genetic Algorithms(GA ) were studied in this paper. applied with the Structural descriptions, Establishing the QSAR models of Drug’s structure and their Activity,and made a equivalent or better results then References.
The main contents can be divided into three as follows:
1. First of all, systematically studied on Several Statistical Models which important in Quantitative Structure Activity Relationship research.
2. Secondly,MEDV /VMEI were employed to characterize molecular structure of active components on Pharmaceutical,and has made some investigation on MEDV /VMEI as the extended and improved.
3. Finally , Establishing QSAR models of several kinds of Pharmaceutical or compounds,which are two sets of anti-HIV agents ,three Series of peptide,three types of estrogen,Promoter of DNA and several kinds of Persistent Organic Pollutants and Alkanes.
The QSAR methods which studied in this paper has made a good result , and as a new way for Pharmaceutical exploitation and design research., will have a bright further.
分子结构表征是定量构效关系研究的一个关键环节,结构描述子能否反映分子与生物活性相关的结构信息,决定了定量构效关系研究的成败。文中对分子电性距离矢量/分子电性相互作用矢量(MEDV/VMEI)等进行深入的研究。
建模方法与技术是定量构效关系研究的一个重要内容。本文主要对多元线性回归(multiple linear regression, MLR)、人工神经网络(artificial neural network, ANN)和遗传算法(Genetic Algorithms ,GA)等几类统计算法进行了研究,与所研究的结构描述子结合,应用于药物的定量构效关系建模研究。
本文研究工作分为三个部分:
第一部分:算法模型的研究,对回归方法(MLR),人工神经网络模型(ANN),遗传算法(GA)等几种构效关系研究中比较重要的统计方法进行研究;
第二部分:分子结构表征模型的研究,主要对分子距边矢量(MDV)模型、分子电性相互作用矢量(VMEI)模型等分子结构描述方法进行讨论,提出改进和完善的方法;
第三部分:结合所研究的分子结构描述方法和算法模型,根据不同药物体系结构与活性内在的函数关系,采用多种统计方法,建立几类药物体系的结构和活性的定量构效关系,取得了良好的效果。所研究的药物体系包括:两个体系的抗艾滋病药物、三个体系的肽类、三类雌激素类药物、DNA启动子以及几类持久性环境污染物和烷烃体系等。
本论文对QSAR药物设计研究中的建模方法和结构描述方法进行了探讨,应用于药物/化合物的构效关系研究,获得了一定的成绩,所建立的相关模型对药物的活性成分筛选和药物设计具有一定的指导作用,值得深入研究。可望通过进一步研究,为药物分子的筛选及其创新药物设计开辟了一条新的道路。
Quantitative Quantitative Structure-Activity Relationship (QSAR), which investigates the quantitative relationship between the molecular structural parameters and biological activities or dependent functions, is one of the most important fundamental fields in pure and applied chemistry and pharmacy. As an important field for basic research and application of modern chemistry,it has become the hotspot of research on organic chemistry, pharmacological chemistry, environment chemistry, chemometrics, molecular biology and immunology.
Structural description is a key step in the QSAR studies. Whether the structural descriptors can reflect the structural variations determines the success of QSAR studies. In this paper, suah as Molecular electro-negativity distance vectors (MEDV) , Molecular Electronegativity Interaction Vector(VMEI) were further Studied.
The modeling methods and related techniques are also important for the success of QSAR studies. The modeling methods such as multiple linear regression (MLR), Artificial Neural Network, Genetic Algorithms(GA ) were studied in this paper. applied with the Structural descriptions, Establishing the QSAR models of Drug’s structure and their Activity,and made a equivalent or better results then References.
The main contents can be divided into three as follows:
1. First of all, systematically studied on Several Statistical Models which important in Quantitative Structure Activity Relationship research.
2. Secondly,MEDV /VMEI were employed to characterize molecular structure of active components on Pharmaceutical,and has made some investigation on MEDV /VMEI as the extended and improved.
3. Finally , Establishing QSAR models of several kinds of Pharmaceutical or compounds,which are two sets of anti-HIV agents ,three Series of peptide,three types of estrogen,Promoter of DNA and several kinds of Persistent Organic Pollutants and Alkanes.
The QSAR methods which studied in this paper has made a good result , and as a new way for Pharmaceutical exploitation and design research., will have a bright further.
引文
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