从植物中寻找农药活性物质

从植物中寻找农药活性物质,判明结构,使之成为创新类型农药有效母体,是创制新农药品种的重要途径之一,受到当今全世界农药界的广泛重视。本文综合性介绍植物与农药的关系,该交叉学科研究的一般程序和方法以及通过对有效母体的结构改造,构一效关系的研究,创制新农药的研究过程。     

黄酮类醛糖还原酶抑制剂的三维定量构效关系研究

目的：建立黄酮类化合物抑制剂活性的三维定量构效关系模型,为进一步进行黄酮类醛糖还原酶抑制剂（ARI）的活性与三维结构关系的研究提供重要依据。方法：采用比较分子力场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,系统研究了75个新型ARI的三维定量构效关系。结果：CoMFA和CoMSIA模型的交互验证相关系数q^2值分别为0.603和0．706、非交互验证相关系数r2值分别为0.956和0.900。结论：CoMFA和CoMSIA模型均具有较强的预测能力,CoMFA和CoMSIA模型的三维等值线图直观地解释了化合物的构效关系,阐明了化合物结构中各位置取代基对黄酮类醛糖还原酶抑制剂活性的影响,为进一步结构优化提供了重要理论依据。     

环烯醚萜类化合物的结构及构效关系研究进展

环烯醚萜作为一类重要有效成分,存在多种中草药中,具有抗肿瘤,抗炎,降糖等多种药理活性。环烯醚萜类化合物结构不同,则其生物活性和作用机制也不尽相同,目前国内外对环烯醚萜类化合物结构修饰及其构效关系归纳总结尚未见文献报道。因此对此类化合物结构及构效关系进行系统归纳总结,将对开发此类创新药物具有较高的研究意义。本文通过国内外广泛的文献调研,分析总结此类化合物的结构、活性、作用机制之间的关系,对此类化合物活性预测、结构优化将具有良好的促进作用,为今后开发具有更高活性的新型环烯醚萜类药物提供研究基础。     

“核糖体失活蛋白抗艾滋病病毒活性及构效关系的研究”获2003年度云南省科学技术奖（自然科学类）二等奖

中国科学院昆明动物研究所郑永唐研究员主持的“核糖体失活蛋白抗艾滋病病毒活性及构效关系的研究”,利用分子生物学和病毒学等手段,系统研究了核糖体失活蛋白(RIP)抗人艾滋病病毒(HIV)活性,并对天花粉蛋白(TCS抗HIV活性的作用机制、构效关系进行了深入的研究。     

海藻多糖生物活性及分子修饰

简要介绍了近年来有关海藻多糖抗病毒、抗肿瘤、免疫促进、免疫抑制等生理活性的最新研究 ,重点介绍了不同种类海藻多糖的不同生理活性机理的研究进展。对多糖与生理活性之间的构效关系进行了阐述 ,在构效关系基础上进行多糖分子结构修饰是提高海藻多糖生理活性、降低毒副作用的有效手段。进一步介绍了目前多糖分子修饰常用方法 ,并对修饰后分子的生理活性改变进行了阐述。     

A型肉毒毒素抑制剂的分子全息定量构效关系研究

目的:建立A型肉毒毒素抑制剂的定量构效关系模型。方法:应用分子全息定量构效关系(HQSAR)技术,研究了14种A型肉毒毒素抑制剂的抑制活性与其二维分子结构之间的关系,讨论了碎片区分参数及碎片长度对模型质量的影响。结果:最佳全息条件下产生的模型相关系数r2为0.780,交叉验证相关系数q2LOO为0.583。所建模型具有良好的拟和效果和较高的预测能力,HQSAR模型贡献图显示抑制剂分子中的噻吩环及羟胺对活性有较大贡献。结论:本研究对新抑制剂的设计具有一定的指导作用。     

天然产物抗氧化构效关系及作用机理的研究概况

本文简介了近十年来天然抗氧化剂的研究概况,讨论了天然产物抗争氧化活性的构效关系及作用机理。     

CD_(126)功能域三维结构研究进展

ＣＤ１２６功能域三维结构的研究对研制具有不同生物学活性的新型ＩＬ－６Ｒ突变体和进行基于ＣＤ１２６三维结构的药物分子设计研究有重要的指导意义。本文概述ＣＤ１２６空间结构预测及同源模建的研究进展,并总结ＣＤ１２６与其配基ＩＬ－６形成高亲和力受体复合物时构－效关系分析的研究动态 。     

CD126功能域三维结构研究进展

ＣＤ１２６功能域三维结构的研究对研制具有不同生物学活性的新型ＩＬ－６Ｒ突变体和进行基于ＣＤ１２６三维结构的药物分子设计研究有重要的指导意义。本文概述ＣＤ１２６空间结构预测及同源模建的研究进展,并总结ＣＤ１２６与其配基ＩＬ－６形成高亲和力受体复合物时构－效关系分析的研究动态。     

紫杉类化合物研究进展

自本世纪70年代从欧洲红豆杉树皮中提取分离得到具有抗癌活性的天然产物紫杉醇以来,关于红豆杉属植物的研究已取得了许多进展。本文就1990年以后发表的有关文献。对该属植物的化学成分研究、合成方法、细胞培养技术及构效关系研究进行综述。     

The universal fuzzy logical framework of neural cir-cuits and its application in modeling primary visual cortex

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.     

Molecular evolution of a peptide GPCR ligand driven by artificial neural networks

Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterative trial-and-error process based on structure-activity relationships or screening of peptide libraries that contain many structural variants of the native molecule. Here, we present a novel neural network architecture for the improvement of metabolic stability without loss of bioactivity. In this approach the peptide sequence determines the topology of the neural network and each cell corresponds one-to-one to a single amino acid of the peptide chain. Using a training set, the learning algorithm calculated weights for each cell. The resulting network calculated the fitness function in a genetic algorithm to explore the virtual space of all possible peptides. The network training was based on gradient descent techniques which rely on the efficient calculation of the gradient by back-propagation. After three consecutive cycles of sequence design by the neural network, peptide synthesis and bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization.     

The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. Although there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.     

Role of structural inhomogeneities in resting-state brain dynamics

Brain imaging methods allow a non-invasive assessment of both structural and functional connectivity. However, the mechanism of how functional connectivity arises in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which functional correlations arise from underlying structural connections taking into account inhomogeneities in the interactions between the brain regions of interest. The local dynamics of a neural population is assumed to be of phase-oscillator type. The considered structural connectivity patterns describe long-range anatomical connections between interacting neural elements. We find a dependence of the simulated functional connectivity patterns on the parameters governing the dynamics. We calculate graph-theoretic measures of the functional network topology obtained from numerical simulations. The effect of structural inhomogeneities in the coupling term on the observed network state is quantified by examining the relation between simulated and empirical functional connectivity. Importantly, we show that simulated and empirical functional connectivity agree for a narrow range of coupling strengths. We conclude that identification of functional connectivity during rest requires an analysis of the network dynamics.     

Neural network analysis of follow-up data in primary breast cancer

This paper reports on the performance of a recently developed neural network environment incorporating likelihood-based optimization and complexity reduction techniques in the analysis of breast cancer follow-up data with the goal of building up a clinical decision support system. The inputs to the neural network include classical factors such as grading, age, tumor size, estrogen and progesterone receptor measurements, as well as tumor biological markers such as PAI-1 and uPA. The network learns the structural relationship between these factors and the follow-up data. Examples of neural models for relapse-free survival are presented, which are based on data from 784 breast cancer patients who received their primary therapy at the Department of Obstetrics and Gynecology, Technische Universit?t München, Germany. The performance of the neural analysis as quantified by various indicators (likelihood, Kaplan-Meier curves, log-rank tests) was very high. For example, dividing the patients into two equally sized groups based on the neural score (i.e., cutoff = median score) leads to an estimated difference in relapse-free survival of 40% or better (80% vs. 40%) after 10 years in Kaplan-Meier analysis. Evidence for factor interactions as well as for time-varying impacts is presented. The neural network weights included in the models are significant at the 5% level. The use of neural network analysis and scoring in combination with strong tumor biological factors such as uPA and PAI-1 appears to result in a very effective risk group discrimination. Considerable additional comparison of data from different patient series will be required to establish the generalization capability more firmly. Nonetheless, the improvement of risk group discrimination represents an important step toward the use of neural networks for decision support in a clinical framework and in making the most of biological markers.