Structure-activity relationships for antiplasmodial activity among aminoquinolines

The extended gravitational index G(Q) and quantum-chemical descriptors were calculated for the relationship analysis of aminoquinolines. An evolutionary algorithm was described for variable selection and building QSAR models. And the quasi-newton neural networks were employed with better results.

Comparison of different methods for variable selection

In chemistry for chemical analysis of a multi-component sample or quantitative structure-activity/property relationship (QSAR/QSPR) studies, variable selection is a key step. In this study, comparisons between different methods were performed. These methods include three classical methods such as forward selection, backward elimination and stepwise regression; orthogonal descriptors; leaps-and-bounds regression and genetic algorithm. Thirty-five nitrobenzenes were taken as the data set. From these structures quantum chemical parameters, topological indices and indicator variable were extracted as the descriptors for the comparisons of variable selections. The interesting results have been obtained. (C) 2001 Elsevier Science B.V. All rights reserved.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

定量结构-活性/性质相关性研究中变量选择方法的研究——遗传算法和几种传统算法的比较

鉴于变量选择在 QSAR/QSPR研究中的重要性 ,比较了遗传算法和几种传统的方法 ,如前进法、后退法及逐步回归法 .结果表明 ,对于研究中所用数据 ,遗传算法较几种传统的方法为好 ,其原因可能由于传统的方法陷入了局部最优 .遗传算法在变量较多的情况下方可显示出效率高和得到较好结果的优越性 .对于变量的选择 ,遗传算法是一值得推荐的有效的方法

三维分子特征的提取及苯胺类化合物结构—毒性相关性研究

在环境中有许多污染物,如多氯联苯,多环芳烃,DDT等,这些化合物具有难降解性,高脂溶性及易生物储积,所以潜在的毒性很大。本文侧重研究的是苯胺类化合物结构与性质相关性(QSAR)。 该类研究的关键是如何有效地表征化合的结构。从80年代以来,在定量构效关系研究中,陆续出现一些考虑生物活性分子与受体结合时三维结构的研究方法,统称为三维定量构效关系(3D-QSAR)研究。其中,最典型应用最普遍的3D-QSAR、方法之一是CoMFA方法。新近,在我们的研究中发现对于某些类的化合物,CoMFA适应性

应用量化参数和拓扑指数研究吡喃酮类化合物结构与活性的相关性

将量子化学参数和拓扑指数共同应用于吡喃酮类化合物的结构与活性的相关分析中,并运用逐步回归分析方法和最佳变量子集算法对变量进行压缩,通过多元回归方法和人工神经网法进行计算分析,获得了比较好的相关模型.

Studies on three-dimensional quantitative structure-activity relationship of PDE inhibitory activity of imidazopyridines

In recent years there has been a resurgence of interest in inhibitors of cyclic nucleotide phosphodiesterases (PDE) and enzymes responsible for the intracellular hydrolysis of the second messenger cAMP and cGMP. In this study, a series of 2-substituted phenyllimidazo[4,5-b]pyridines have been made to investigate 3D-QSAR of PDE activity using CoMFA. CoMFA resulted in a quantitative description of the major steric and electrostatic field effects, and gave significant new insights to factors governing PDE inhibition activity. The model was used to predict the PDE inhibition activity of imidazopyridines with satisfactory results.

Quantitative structure-property relationships for colour reagents and their colour reactions with cerium using computational neural networks

Quantitative structure-activity/property relationships (QSAR/QSPR) studies have been exploited extensively in the designs of drugs and pesticides, but few such studies have been applied to the design of colour reagents. In this work, the topological indices A(x1)-A(x3) suggested in this laboratory were applied to multivariate analysis in structure-property studies. The topological indices of 43 phosphone bisazo derivatives of chromotropic acid were calculated. The structure-property relationships between colour reagents and their colour reactions with cerium were studied using A(x1-Ax3) indices with satisfactory results. The purpose of this work was to establish whether QSAR can be used to predict the contrasts of colour reactions and in the longer term to be a helpful tool in colour reagent design.

烟酸、吖啶、黄连素对热浸镀钢材的缓蚀作用及其量化构效关系

为了找到在热浸镀钢材表面清洗处理过程中替代铬酸的缓蚀、清洗剂，本论文通过量子化学计算方法、失重实验、电化学测试和扫描电镜分析等方法系统研究了环境友好型缓蚀剂——烟酸、吖啶和黄连素对热浸镀锌、5%铝-锌和55%铝-锌钢材在盐酸介质中的缓蚀作用及其量化构效关系。 量化计算结果表明，这三种杂环化合物均具有多个吸附活性中心的平面结构。Mulliken电荷、最高占据轨道（HOMO）能量和最低空轨道（LUMO）能量分布显示活性中心主要集中在氧原子、氮原子和杂环周围。三种化合物可能通过这些活性中心吸附在镀层表面以阻止镀层电极反应，且其前线轨道同镀层表面锌原子的前线轨道能够相互作用，使得杂环化合物分子可通过在镀层钢材表面形成吸附膜而阻止镀层表面锌在盐酸介质中的溶解。 失重和电化学测试的结果表明，三种化合物对三种热浸镀钢材在盐酸介质中均是高效的环境友好型缓蚀剂，最高缓蚀效率可达99%以上；其中黄连素的缓蚀效果最好，在浓度为1.0×10-4M时缓蚀效率就已达到80%以上。实验结果同时表明，三种缓蚀剂通过单分子层的化学吸附方式吸附在镀层表面以阻滞酸液对镀层的腐蚀，其吸附遵从Langmuir吸附等温式。扫描电镜分析结果也显示了在盐酸介质中三种缓蚀剂能够很好地抑制镀层的腐蚀。几种实验方法得到的结果能够很好地吻合，同时又都验证了量子化学计算的推测。 另外，通过恒电量方法和量化构效关系(QSAR)研究了盐酸介质中烟酸、吖啶和黄连素对热浸镀钢材的缓蚀机理。恒电量实验验证了失重实验和电化学测试的结果：烟酸、吖啶和黄连素是混合型缓蚀剂，三种缓蚀剂通过活性中心吸附在镀层材料表面，同时抑制镀层的阴阳极反应以减缓镀层在盐酸溶液中的腐蚀。量化构效关系显示缓蚀效率与最高占据轨道能量（EHOMO）成正相关关系，与前线轨道能量差ΔE（ELUMO-EHOMO）成负相关关系；三种缓蚀剂通过提供电子与镀层材料表面锌原子相互作用而起到缓蚀作用。 最后，以铬酸清洗方法作参照，使用缓蚀酸液清洗方法对热浸镀钢材的腐蚀产物进行去除，结果显示，缓蚀酸液清洗方法可替代铬酸对热浸镀钢材进行腐蚀产物去除和绿色清洗。

Investigation on inhibition behavior of S-triazole-triazole derivatives in acidic solution

Four main methods, such as weight loss test, EIS, adsorption isotherm and quantum chemical calculation were employed to study the inhibition efficiency and mechanism of three derivatives on mild steel in acid solution, whose inhibition efficiency were proved to follow the order of DMTT > NMTT > PMTT, The adsorption model of DMTT was established at different temperature according to the fitted results. The quantum chemical results indicated that the adsorption sites of the derivatives were strongly centralized on benzene ring, triazole ring, etc. QSAR was set up to explain the relationship of molecular structure and the inhibition effect of the derivatives. (C) 2008 Elsevier Ltd. All rights reserved.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.