黄酮类化合物抗氧化活性的构效关系研究

自由基具有强烈的氧化能力,研究寻找自由基清除剂越来越引起人们的重视。本文对已有实验报道的10种黄酮的结构进行了几何优化,并进行了电子结构计算,以实验测定的黄酮类化合物清除对苯半醌自由基能力的高低,即清除率数据为因变量,以量化计算得到的电子结构指数为自变量,用相关分析和逐步回归方法加以讨论,从理论上对实验结果进行了较为圆满的解释,并得出了较为合理的定量构效关系(QSAR)方程,为设计高活性抗氧化性黄酮类化合物提供了理论参考。     

黄酮类化合物抗氧化活性的神经网络模型

采用量子化学的算法,计算了18个黄酮类化合物的13个结构参数,并采用逐步回归分析,筛选出3个电性结构参数,即A环的总电荷、B环的总电荷、双键氧上的电荷。使用Matlab7.0软件中的神经网络工具箱建立黄酮类化合物抗氧化活性的神经网络模型。交互验证法预报结果表明,预报误差平方和(PRESS)为0.0192,交叉验证系数(R)2为0.875,该神经网络模型预报结果可靠,神经网络可作为化合物活性研究的有效计算机辅助手段。     

抗TB活性化合物的研究

结核病（tuberculosis,TB）是由结核分枝杆菌（mycobacterium tuberculosis,MTB）引起的一种缓慢性致死疾病。虽然目前结核病在发达国家发病率较低,但在发展中国家仍然是高发的重大传染性疾病。基于此种状况,寻找新的活性化合物或对现有药物分子进行改进,成为当下开发抗TB新药的热点。目前,在研的化合物包括喹啉类、喹诺酮类、咪唑类、苯并噻嗪酮类、唑烷酮类以及天然产物等,其中喹啉类化合物依然是重要研究对象。这些化合物大多数具有低微摩尔的体外抗结核活性,最有可能对体内药物敏感菌株或耐药菌株有效。本文详细介绍了2014~2017年间抗结核化合物的研究现状,就其化学结构特点、抗结核活性、构效关系和安全性等方面进行综述,并展望了该领域今后的发展方向。     

黄酮类化合物结构修饰及抗肿瘤活性研究进展

我国天然药物和中药资源丰富,种类繁多,可作为先导化合物进行修饰以提高可成药性。其中,黄酮类化合物是自然界中一种常见的天然多酚类化合物,在抗肿瘤方面具有其独特的生物活性;对其进行结构修饰与改造,可提高黄酮类化合物的生物利用度和抗肿瘤活性。本文通过查阅并整理近几年国内外的黄酮类化合物的相关文献,对黄酮类化合物的母核位点进行结构修饰与改造所得的101个黄酮类衍生物及其抗肿瘤活性及作用机制进行综述,同时讨论了构效关系,以期为黄酮类衍生物的结构修饰和抗肿瘤研究提供参考和帮助。     

含氟新农药的构效关系研究Ⅰ.经典QSAR分析

为了能更深入地认识含氟新化合物作为农药的生物活性和其结构间的关系,建立有意义的构效关系模型,我们用经典QSAR(定量构效关系)方法研究了三十三个含氟化合物的两种不同的生物活性与结构的关系,其对抗黄瓜疫病活性模型有很好的解释能力和预测能力,并根据这个模型设计了一些新的活性结构.而对抑制西瓜白绢病的活性数据的处理未能获得理想模型.通过这一工作确立了先应用聚类等定性分析方法,再用多元统计分析方法作更深入研究的QSAR研究模式.     

新黄酮类化合物的脱甲基反应及其抗氧化活性

以3,4-二甲氧基苯甲醛(1)为起始原料,使用文献已报道的三氟乙酸法合成了3个含两个甲氧基取代基的新黄酮类化合物4a-4c,在新制碘化铝的催化下发生脱甲基反应,合成了3个含多个羟基取代基的新黄酮类化合物5a-5c,产率均高于90%.所有目标化合物均经过IR,~1H NMR,ESI-MS对其进行了结构确认.通过测定羟基自由基和DPPH自由基清除活性两个方面评价其抗氧化活性,结果发现,脱甲基后产物活性均增强.     

N—亚硝基化合物的结构／致癌活性的三维构效关系研究

用比较力场分析研究了Ｎ－亚硝基化合物的结构与致癌活性的关系，考察了网络结构和探针原子对结果的影响。结果表明，立体效应和静电作用场是描述其致癌活性和进行结构性能关系研究的最重要的结构参数。     

酪醇酯衍生物抗氧化活性的QSAR研究

抗氧化剂是防止食品氧化、提高食品稳定性的一类食品添加剂。为了研究25种酪醇酯衍生物对Fe~(3+)-TPTZ配合物抗氧化活性Fr的定量构效关系(QSAR),按照分子的拓扑环境编程计算了上述化合物的电性距离矢量(M_d)。通过最佳子集变量回归方法,建立了抗氧化活性的三参数(M_(32)、M_(59)、M_(77))QSAR模型,传统相关系数(R~2)与逐一剔除法交叉验证相关系数(R_(cv)~2)分别为0.939、0.815。经R_(cv)~2、V_(IF)、F_(IT)、A_(IC)、F诊断,显示良好的稳健性和预测能力。根据进入模型的3个变量可知,影响酪醇酯衍生物对Fe~(3+)-TPTZ配合物抗氧化活性的主要结构基团是酚羟基、C-和-NH-等。     

Multi-KNN-SVR组合预测在含氟化合物QSAR研究中的应用

为深入认识含氟农药生物活性与其结构之间的关系, 建立了理想的QSAR模型, 从化合物油水分配系数等7个分子结构描述符出发, 基于支持向量回归(SVR)和MSE最小原则, 经自动寻找最优核函数和非线性筛选描述符, 构建了多个K-最近邻(KNN)预测子模型. 再经非线性筛选获得保留子模型, 以保留子模型实施组合预测(Multi-KNN-SVR). 33种含氟化合物对5种不同病害生物活性的留一法组合预测结果表明, 采用非线性筛选描述符和KNN子模型能有效地提高预测精度, 基于多个KNN子模型的非线性组合能进一步提高预测性能. Multi-KNN-SVR组合预测在QSAR以及其它相关预测研究中具有广泛应用前景.     

Chemometric QSAR studies of antifungal azoxy compounds

Summary Quantitative structure-activity relationships (QSARs) for 16 azoxy compounds with antifungal activity have been studied by the combined approach of a partial least-squares method and factorial design. The PLS model equation suggested the structural requirements of two substituents, R₁ and R₂, for the antifungal activity. The sterically bulky and hydrophobic R₁ substituents and electron-withdrawing R₂ substituents are favorable for the activity. We propose candidate compounds which are more potent than the compounds based on QSAR data. In this study, we show that the chemometric approach is a powerful tool for QSAR studies and drug design.Abbreviations PLS partial least squares - FD factorial design - MLR multiple linear regression - PPs principal properties     

Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction

This study gives a quantitative structure-activity relationship (QSAR) correlation of the 72 N-benzylsalicylamide derivatives properties with their antimycobacterial activity. The antimycobacterial activity was measured as the minimal inhibition concentration (MIC) determined for four strains of mycobacterium (M. avium, M. kansasii, M. kansasii clin.-clinically isolated form, and M. tuberculosis) after 14 days and after 21 days of cultivation. The objective was to identify the factors most closely defining biological activity of N-benzylsalicylamides, in order to enable QSAR prediction of new derivatives with high antimycobacterial activity. Optimal properties for the QSAR analysis were selected from several physicochemical properties, including lipophilicity parameter log P, molecular mass M, molar refraction MR, NMR chemical shifts, polarizability, etc. Many of the considered properties are different from those typically used in traditional QSAR. Selection of the most important properties was performed by one-way Analysis of Variance (ANOVA) and correlation analysis using the significance coefficients and the correlation coefficients, respectively. The chosen variables were further used in artificial neural networks (ANN) for predicting biological activity in the form of-log(MIC). Presented at the 1st International Conference “Applied Natural Sciences” on the occasion of the 10th anniversary of the University of Ss. Cyril and Methodius, Trnava, 7–9 November 2007.     

QSAR based modeling of inhibitory activity of alkenyldiarylmethane derivatives

A series of alkenyldiarylmethanes (ADAMs) were subjected to QSAR analysis by using linear free energy relationship model of Hansch. QSAR has been developed using steric, electronic and topological parameters along with appropriate dummy variable. Statistical techniques were applied to identify the structural and physicochemical requirements for ADAMs. The results are critically discussed on the basis of regression data and cross-validation techniques.     

硝基芳烃对梨形四膜虫急性毒性的定量构效关系研究

硝基芳烃衍生物是炸药、染料、农药和有机合成的重要原料或中间体,应用极为广泛,但它们均为有毒物质,是我国松花江中主要有机污染物之一,其中有些化合物已被美国环保局列为优先监测污染物[1].     

Synthesis,antimicrobial activity and QSAR studies of 2,5-disubstituted benzoxazoles

In this study, a new series of 2,5-disubstituted benzoxazoles was synthesized and their structures were elucidated by elemental analysis, MASS, ¹H-NMR, ¹³C-NMR and IR spectral data. Newly and previously synthesized 2,5-disubstituted benzoxazole derivatives were evaluated for antibacterial and antifungal activity against standard strains and their drug-resistant isolates. Microbiological results showed that the compounds presented a large spectrum of activity having MIC values of 250–7.8 µg mL^?1 against the tested microorganisms. Among the newly synthesized derivatives 3–22, compound 11 was the most active against Candida krusei out of all; however, it was one dilution less potent than standard drug fluconazole. In addition, all the new and previous compounds were more active than standard drugs ampicillin trihydrate and rifampicin against Pseudomonas aeruginosa and its gentamicin-resistant isolate. The 2D-QSAR (Quantitative Structure–Activity Relationship) analysis of a set of newly and previously synthesized benzoxazoles tested for growth inhibitory activity against methicillin-resistant Staphylococcus aureus (MRSA) was also performed by using multivariable regression analysis. The activity contributions for substituent effects of these compounds were determined from the correlation equation for predictions of the lead optimization.     

Determination of phenolic compounds and their antioxidant activity in fruits and cereals

Three methods, FCM (with Folin-Ciocalteu reagent), PBM (Price and Butler) and AAPM (with 4-aminoantipyrine) for assessment of phenolic compounds and three commonly used methods, TEAC (Trolox equivalent antioxidant capacity), DPPH (with diphenyl-picrylhydrazyl radical), and FRAP (ferric reducing antioxidant power) for evaluation of antioxidant capacity, were modified to a semimicroscale (total volume 1 ml) with minimum consumption (to 100 μl) of a sample and thereby applicable for fast screening. Appropriate standards and extracts of 17 kinds of fruit and six kinds of cereal were assessed for total content of phenolic compounds and total antioxidant capacity by each of these methods. The results of analyses of commonly used standards (gallic, caffeic and ferulic acids, (+)-catechin, Trolox, fenol and FeSO₄) for these methods and identical plant extract showed different reactivity of principal reagent of the methods with individual standards and therefore with phenolic substances of extracts as well. However, the trends of the measured values of extracts could be compared, though their absolute values differ proportionally. At assessments of phenolic compounds it is important to determine content of ascorbic acid at roughly the same time and correct the obtained values according to its contribution to the increase in absorbance calculated on the basis of absorbance equations, especially for samples with a higher content. The same is true for reducing saccharides; they can significantly “elevate” values of contents of phenolic compounds and antioxidant activities (by even more than 50%), especially in samples of sweeter fruits. The saccharides should therefore be removed or a correction applied reflecting their concentration.