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Chemometric Analysis of Some Biologically Active Groups of Drugs on the Basis Chromatographic and Molecular Modeling Data

机译：基于色谱和分子模拟数据的一些生物活性药物的化学计量分析

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In this work, three different groups of drugs such as 12 analgesic drugs, 11 cardiovascular system drugs and 36 "other" compounds, respectively, were analyzed with cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) methods. All chemometric analysis were based on the chromatographic parameters (logk and logk(w)) determined by means of high-performance liquid chromatography (HPLC) and also by molecular modeling descriptors calculated using various computer programs (HyperChem, Dragon, and the VCCLAB). The clustering of compounds were obtained by CA (using various algorithm as e.g. Ward method or unweighted pair-group method using arithmetic averages as well as Euclidean or Manhattan distance), and allowed to build dendrograms linked drugs with similar physicochemical and pharmacological properties were discussed. Moreover, the analysis performed for analyzed groups of compounds with the use of FA or PCA methods indicated that almost all information reached in input chromatographic parameters as well as in molecular modeling descriptors can be explained by first two factors. Additionally, all analyzed drugs were clustered according to their chemical structure and pharmacological activity. Summarized, the performed classification analysis of studied drugs was focused on similarities and differences in methods being used for chemometric analysis as well as focused abilities to drugs classification (clustering) according to their molecular structures and pharmacological activity performed on the basis of chromatographic experimental and molecular modeling data. Thus, the most important application of statistically important molecular descriptors taken from QSRR models to classification analysis allow detailed biological (pharmacological) classification of analyzed drugs.
机译：在这项工作中，分别通过聚类分析（CA），主成分分析（PCA）和因子分析（FA）方法分析了三类不同的药物，例如12种止痛药，11种心血管系统药物和36种“其他”化合物。 。所有化学计量分析均基于通过高效液相色谱（HPLC）以及使用各种计算机程序（HyperChem，Dragon和VCCLAB）计算的分子模型描述符确定的色谱参数（logk和logk（w））。通过CA（使用Ward方法或使用算术平均值的非加权对-群方法以及算术平均值以及Euclidean或Manhattan距离的各种算法）获得化合物的簇，并讨论了建立具有相似理化和药理性质的药物的树状图链接药物。此外，使用FA或PCA方法对分析的化合物组进行的分析表明，输入色谱参数以及分子建模描述符中几乎到达的所有信息都可以由前两个因素来解释。此外，所有分析药物根据其化学结构和药理活性进行聚类。综上所述，对所研究药物进行的分类分析重点在于用于化学计量学分析的方法的相似性和差异，以及根据其分子结构和在色谱实验和分子生物学基础上进行的药理活性进行的药物分类（聚类）的重点能力。建模数据。因此，取自QSRR模型的统计学上重要的分子描述符在分类分析中的最重要应用允许对所分析药物进行详细的生物学（药理学）分类。

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