电子参数σ及其在QSAR中的应用

本文阐述了ＱＳＡＲ研究中常用的几个电子参数σ的定义、测试方法及估算方法，对可利用数据进行了归纳整理，并通过实例阐述了其在ＱＳＡＲ中的重要作用。     

分子拓扑学参数及其在定量结构——活性相关(QSAR)研究中的应用

本文阐述了定量结构—活性相关研究中常用的几个拓扑学参数的定义及其计算方法,对相关数据进行了归纳整理,并通过实例阐述了拓扑学参数在ＱＳＡＲ研究中的重要作用。     

有机物的结构──活性定量关系及其在环境化学和环境毒理学中的应用

有机物的结构──活性定量关系及其在环境化学和环境毒理学中的应用王飞越（北京大学城市与环境学系，北京１００８７１）陈雁飞（武汉大学环境科学系）最近几十年来，有机物结构──活性定量关系研究（ＱＳＡＲ，ＱｕａｎｔｉｔａｔｉｖｅＳｔｒｕｃｔｕｒｅ－Ａｃｔｉｖ...     

有机物生物降解性QSBR/SBR方法初探

定量／ 定性结构生物降解性相关关系（ＱＳＢＲ／ＳＢＲ） 对研究有机物的生物降解性具有重要意义。本文比较了七种常用的ＱＳＢＲ／ＳＢＲ 模型方法,结合实例对其原理、优缺点等进行了论述。     

有机化合物动态定量结构——生物降解关系(QSBR)模型研究

本文通过分析一般ＱＳＢＲ模型应用的特点、不足和影响有机物生物降解的基本因素，指出ＱＳＢＲ模型应综合反映影响有机物生物降解的四个方面（以氯代芳香化合物为例）：氯代苯系物的摄入、氯代苯系物的诱导作用、毒性物质的形成和基础酶的缺乏。在此基础之上给出了动态ＱＳＢＲ的概念模式及方法学基础。     

SBR法处理印染废水的研究

应用ＳＢＲ法、ＰＡＣ－ＳＢＲ法，微电解－ＳＢＲ法对印染废水进行了对比处理试验研究。试验结果表明：微电解－ＳＢＲ法处理效果优于其它两种方法，当进水ＣＯＤ＝１０００～１６００ｍｇ／Ｌ，色度＝２００～８００倍，ＢＯＤ５＝２５０～４００ｍｇ／Ｌ时．ＣＯＤ去除率在８５％左右，ＢＯＤ５去除率和脱色率均在９０％以上，出水达到排放标准。     

活性硅酸混凝剂PFASSC处理造纸废水

利用硅酸钠、硫酸铝、三氯化铁和硫酸为主要原料制备了复合型活性硅酸混凝剂ＰＦＡＳＳＣ并对造纸废水进行了处理，得到活性硅酸混凝剂ＰＦＡＳＳＣ最佳配方为：ＰＨ＝２．０（Ｆｅ＾３＋＋Ａｌ＾２）／ＳｉＯ２（摩尔比）＝１．５，Ｆｅ＾３＋／Ａｌ＾３＋（摩尔比）＝１．０，与常用混凝剂相比，ＰＦＡＳＳＣ混凝剂处理造纸废水对除浊、脱色和去除ＣＤＤＣｒ有更加优良的性能。     

SBR生化系统的应用及其发展

本文综述了ＳＢＲ的各种工艺运行模式，指明了ＳＢＲ工艺在废水处理中的应用前景和发展方向。     

DHDPS的细胞表达、提纯和结晶

ＤＨＤＰＳ是赖氢酸合成通路中第一步的合成酶。将中国春小麦的ＤＨＤＰＳ基因质粒移植入ＲＤＡ８中，得到ＲＤＡ８／ｐＤＢ２６菌株，本研究通过细胞培养、层析提纯和结晶条件的探索，给出了一个较好的技术路线，并为开展义衍射分析蛋白结构创造了条件，通过该研究，对中国春小麦ＤＨＤＰＳ和野种大肠杆菌的ＤＨＤＰＳ差异有了一定的了解，并且对细胞培养的ＭＭ介质处理，ＤＥＡＥ－Ｓｅｐｈａｒｏｓｅ，Ｐｈｅｎｙｌ－Ｓｅｐｈａｒｏｓｅ和ＭｏｎｏＱ层析的方法给出具体实验条仲。酶活的检测和蛋白浓度测定都是采取高灵敏度的方法。结晶的ＳＣｒｅｅｎｉｎｇ条件对于二种ＤＨＤＰＳ有很大的差异。对野种大肠杆茵的ＤＨＤＰＳ，需要表面活性剂Ｎ－ｏｃｔｙｌ－Ｄ－ｇｌｕｃｏｐｙｒａｎｏｓｉｄｅ，ｐＨ１０．０～１０．５，对于中国春小麦一ＤＨＤＰＳ则未找到较好的表面活性剂，ｐＨ６．８～７．６。中国春小麦一ＤＨＤＰＳ晶体培养条件在以往文献中未见报导。     

EPA－8260方法测定水中VOC的质量保证

为提高使用ＥＰＡ一８２６０法测定水中卤代烃化合物所取得的数据的可靠性和保证预定的质量保证（ＱＡ）和质量控制（ＱＣ）值，本文作者根据自己的实践经验，提出在测定过程中应采取的措施。     

QSAR models for estimating properties of persistent organic pollutants required in evaluation of their environmental fate and risk

The molecular connectivity indices (MCIs) have been successfully used for over 20 years in quantitative structure activity relationships (QSAR) modelling in various areas of physics, chemistry, biology, drug design, and environmental sciences. With this review, we hope to assist present and future QSAR practitioners to apply MCIs more wisely and more critically. First, we have described the methods of calculation and systematics of MCIs. This section should be helpful in rational selection of MCIs for QSAR modelling. Then we have presented our long-term experience in the application of MCIs through several characteristic and successful QSAR models for estimating partitioning and chromatographic properties of persistent organic pollutants (POPs). We have also analysed the trends in calculated MCIs and discussed their physical interpretation. In conclusion, several practical recommendations and warnings, based on our research experience, have been given for the application of MCIs in the QSAR modelling.     

酚类化合物对硝化颗粒污泥活性抑制的定量结构-活性相关研究

测定了酚类化合物对硝化颗粒污泥活性抑制的logIC50值,以量子化学参数为自变量,应用偏最小二乘法（PLS）,建立了酚类化合物对硝化颗粒污泥活性抑制的定量结构-活性相关（QSAR）模型。模型所提取的PLS主成分所能解释的因变量总方差的比例Qc2um为0.820,表明模型具有较好的稳定性和预测能力。模型的结果表明,影响酚类化合物对硝化颗粒污泥活性抑制的主要因素是logkow、CCR和Ehomo,酚类化合物对硝化颗粒污泥活性抑制的logIC50随着分子logkow的增大而减小,随着Ehomo和CCR的增大而增大。     

Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity

A QSAR model is successfully proposed to predict the toxicity effect on Photobacterium phosphoreum by nonpolar-narcotic-chemical mixtures and/or polar-narcotic-chemical mixtures. For nonpolar-narcotic-chemical mixtures and polar-narcotic-chemical mixtures, their corresponding hydrophobicity-based QSAR models are derived from regression analysis. Comparison of these two QSAR models make us believe that it is the joint effect of hydrogen bond in polar-narcotic-chemical mixture that leads to the difference between these two models. Such joint effect of hydrogen bond can be quantified as AMH and BMH by using the different partition coefficients of mixtures in various organic phase/water systems. And the regression analysis results convinced us that the introduction of AMH does improve the quality of the QSAR model with r2=0.948, S.E.=0.166 and F=745.201 at P=0.000 for total 84 mixtures.     

A comparison of DEMETRA individual QSARs with an index for evaluation of uncertainty

Quantitative structure-activity relationships (QSARs) urgently need to be applied in regulatory programs. Many QSAR models can predict the effect of a wide range of substances to different endpoints, particularly in the case of ecotoxicity, but it is difficult to choose the most appropriate model on the basis of the requirements of the application. During the EC-funded project DEMETRA (www.demetra-tox.net) a huge number of QSAR models have been developed for the prediction of different ecotoxicological endpoints. DEMETRA individual models on rainbow trout LC50 after 96 h, water flea LC50 after 48 h and honey bee LD50 after 48 h have been used as a QSAR database to test the advantages of a new index for evaluating model uncertainty. This index takes into consideration the number of outliers (weighted on the total number of compounds) and their root mean square error. Application on the DEMETRA QSAR database indicated that the index can identify the models with the best performance with regard to outliers, and can be used, together with other classical statistical measures (e.g., the squared correlation coefficient), to support the evaluation of QSAR models.     

A protocol to select high quality datasets of ecotoxicity values for pesticides

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.     

The effect of nitroaromatics' composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis

Novel 1D QSAR approach that allows analysis of non-additive effects of molecular fragments on toxicity has been proposed. Twenty-eight nitroaromatic compounds including some well-known explosives have been chosen for this study. The 50% lethal dose concentration for rats (LD50) was used as the estimation of toxicity in vivo to develop 1D QSAR models on the framework of Simplex representation of molecular structure. The results of 1D QSAR analysis show that even the information about the composition of molecules provides the main trends of toxicity changes. The necessity of consideration of substituents' mutual impacts for the development of adequate QSAR models of nitroaromatics' toxicity was demonstrated. Statistic characteristics for all the developed partial least squares QSAR models, except the additive ones are quite satisfactory (R2=0.81-0.92; Q2=0.64-0.83; R2 test=0.84-0.87). A successful performance of such models is due to their non-additivity i.e. possibility of taking into account the mutual influence of substituents in benzene ring which plays the governing role for toxicity change and could be mediated through the different C-H fragments of the ring. The correspondence between observed and predicted by these models toxicity values is good. This allowing combine advantages of such approaches and develop adequate consensus model that can be used as a toxicity virtual screening tool.     

A Protocol to Select High Quality Datasets of Ecotoxicity Values for Pesticides

Abstract

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.     

Progressive study and robustness test of QSAR model based on quantum chemical parameters for predicting BCF of selected polychlorinated organic compounds (PCOCs)

Systematic analyses on the effects of chemical structures of 31 polychlorinated organic compounds (PCOCs) on their bioconcentration behavior in rainbow trout (Oncorhynchus mykiss) were conducted using quantitative structure-activity relationship (QSAR) techniques. The cluster analyses of individual variables as well as the quality control chart of QSAR model implies the existence of outliers, while the simulation model excluding such samples showed an extreme robustness even if it was tested with different methods. Furthermore, the quantum chemical parameters entering into QSAR model were used to describe the bioconcentration pathways, and the results indicated that bioconcentration behaviors of selected compounds were complicated processes involving permeation stages as well as bio-chemical reaction stages.