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QSAR prediction of antagonistic activity of PCBs towards human PXR by using heuristic method and best subset modeling

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Science China Chemistry 2012年第7期55卷 1459-1466页

作者： ZHANG YiMing YANG XuShu SUN Cheng wang liansheng School of Basic Medical Sciences Nanjing Medical UniversityNanjing 210029China School of Pharmacy Nanjing Medical UniversityNanjing 210029China State Key Laboratory of Pollution Control and Resources Reuse School of Environment Nanjing UniversityNanjing 210093China

Polychlorinated biphenyls(PCBs) can antagonize human pregnane X receptor(hPXR) *** chemicals could pose a serious threat to the reproductive and developmental ability of *** quantitative structure activity relationship(QSAR) provides a promising method for the estimation of PCBs' antagonistic *** this investigation,a QSAR model was developed by using heuristic method and best subset modeling(r2 = 0.873,q2LOO=0.742).The built model was validated externally by splitting the original data set into training and prediction *** results of the model derived are as follows:r2 = 0.907,q2LOO=0.709,r2pred=0.676,suggesting developed QSAR model had good robustness and predictive *** applicability domain(AD) of the model was assessed by Williams *** antagonistic activity(?logKi) of 108 PCBs,which are unavailable by experiment at present,was predicted within the applicability domain of the *** critical structural features related to the activity of PCBs were identified.

关键词： polychlorinated biphenyls (PCBs) human pregnane X receptor (hPXR) heuristic method best subset modeling method quantitative structure-activity relationship (QSAR)

Prediction of gas chromatographic retention indices of organophosphorus compounds by holographic QSRR

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Chinese Science Bulletin 2009年第4期54卷 635-641页

作者： wang HaiYan ZHANG AiQian SUN Cheng wang liansheng State Key Laboratory of Pollution Control and Resources Reuse School of the Environment Nanjing UniversityNanjing 210093 China

A new molecular structure representation, molecular hologram, is employed to investigate the quantitative relationship between gas chromatographic retention indices and molecular structures for 41 methylesterified organophosphorus compounds (OPs). The quantitative structure-retention relationship (QSRR) model has been constructed for GC-RI of the selected OPs through partial least squares regression, which shows high statistical quality and predictive value with non-cross validation correlation coefficient r2 of 0.994, and cross validation correlation coefficient q2LOO values of 0.984. In order to verify the robustness and prediction capacity of the model, 30 OPs were randomly selected from the database as the training set, while the rest were used as the testing set. The result of PLS regressive analysis of the training set yields r2 of 0.995 and q2LOO of 0.982, suggesting the excellent ability to predict the GC-RIs of OPs in the testing set. Furthermore, the retention behavior of the compounds in GC stationary phase is discussed, and the effects of different groups on the OP side-chain in the interaction between OPs and the stationary phase are explored using HQSAR color code, which provides useful guideline for the retention rules of OPs and related compounds.

关键词：有机磷化合物分子全息图定量结构保留关系分子结构

Three-Dimensional Quantitative Structure-Activity Relationships of flavonoids and estrogen receptors based on docking

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Chinese Science Bulletin 2010年第15期55卷 1488-1494页

作者： WU Yang wang Yong ZHANG AiQian YU HongXia wang liansheng State Key Laboratory of Pollution Control and Resources Reuse School of Environment Nanjing University Nanjing 210093 China Shanghai Institute of Material Medical Shanghai 201203 China

Flavonoids are endocrine disrupting compounds that occur ubiquitously in foods of plant *** Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) model based on ligand-receptor docking is established between 20 flavonoids and estrogen receptor alpha (ERα),which may provide further theoretical basis for research on the relationship between flavones and *** molecular field analysis (CoMFA) was employed and the best results of cross-validation and non cross validation were 0.845 and 0.988,***,molecular similarity index analysis (CoMSIA) was employed and the results of cross-validation and non cross validation were 0.670 and 0.990,*** CoMFA/CoMSIA and docking results reveal the structural features for estrogen activity and key amino acid residues in binding pocket,and provide an insight into the interaction between the ligands and these amino acid residues.

关键词：三维定量构效关系雌激素受体α 类黄酮对接 CoMFA 氨基酸残基交叉验证基础

Back-propagation network improved by conjugate gradient based on genetic algorithm in QSAR study on endocrine disrupting chemicals

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Chinese Science Bulletin 2008年第1期53卷 33-39页

作者： JI Li wang XiaoDong YANG XuShu LIU ShuShen wang liansheng State Key Laboratory of Pollution Control and Resources Reuse School of Environment Nanjing University Nanjing 210093 China Key Laboratory of Yangtze River Water Environment Ministry of Education College of Environmental Science and Engineering Tongji University Shanghai 200092 China

Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural net-works (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright applica-tion prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This re-sulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q2pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

关键词：化学药物内分泌人造神经网络遗传算法

Holographic QSAR of environmental estrogens

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Science China Chemistry 2005年第2期48卷 156-161页

作者： wang Xiaodong XIAO Qianfen CUI Shihai LIU Shushen YIN Daqiang wang liansheng State Key Laboratory of Pollution Control and Resources Reuse School of the Environment Nanjing University Nanjing 210093 China State Key Laboratory of Pollution Control and Resources ReuseSchool of the Environment Nanjing University Nanjing 210093 China State Key Laboratory of Pollution Control and Resources ReuseSchool of the Environment Nanjing University Nanjing 210093 China State Key Laboratory of Pollution Control and Resources ReuseSchool of the Environment Nanjing University Nanjing 210093 China State Key Laboratory of Pollution Control and Resources ReuseSchool of the Environment Nanjing University Nanjing 210093 China State Key Laboratory of Pollution Control and Resources ReuseSchool of the Environment Nanjing University Nanjing 210093 China

Experimental and epidemiological studies suggest that some man-made and natu- rally occurring chemicals related to the environment have the potential to interrupt normal func- tioning of the endocrine systems of humans and wildlife. These chemicals, termed EDCs (Endo- crine disrupting Chemicals), pose serious threats to the reproductive capability of humans and wildlife. Because of the structural diversity and various types, development of structure-based rapid screening methodologies is important and necessary for the assessment of the environ- mental pollutants. In this paper molecular hologram based QSAR models were developed with the combinatory application of partial least square (PLS) regression for a large diverse set of 105 environmental estrogens. Quantitatively predictive models were developed based on only mo- lecular structures, which can be used for the accurate prediction of estrogenicity to rapidly screen potential environmental endocrine disrupting chemicals.

关键词： EDCs, RBA, molecular hologram, QSAR, CoMFA.

Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols

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Chinese Science Bulletin 2006年第22期51卷 2717-2723页

作者： wang Bin YU Gang ZHANG Zulin HU Hongying wang liansheng Department of Environmental Science and Engineering POPs Re- search Centre Tsinghua University Beijing 100084 China State Key Laboratory of Pollution Control and Resource Reuse School of Environment Nanjing University Nanjing 210093 China

Alkanols，是麻醉混合物，被学习。到 Photobacteriumphosphoreum 的 15 alkanols 的尖锐毒性(15min-EC50 ) 有 hydrophobicity 的大跨度，被测量。alkanols 的单个毒性的量的结构活动关系(QSAR ) 分析被使用 octanol 水分配系数(Iog/Cow ) 进行。Thepolynomial 表示被使用而不是线性方程开发 QSAR 模型，并且有一个好预兆的潜力的一个 QSAR 模型被完成。而且， alkanols 的混合毒性被学习。为了预言，连接包含很恐水病的 alkanols 的混合物的毒性， octanol 水分配系数的调整被执行，就体积效果的影响而言。并且相等的 octanol 水分配系数被介绍。混合毒性的 QSAR 模型被使用相等的混合 octanol 水分配系数开发。Theresult 证明甚至一个线性模型能由使用相等的混合 octanol 水分配系数预言混合毒性很好。

关键词：烷醇 QSAR 混合毒性等效辛醇-水划分系数疏水性

Quantitative structure—activity relationship 0t estrogen activities of bisphenol A analogs

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Chinese Science Bulletin 2006年第3期51卷 287-292页

作者： CUI Shihai LIU Shushen YANG Jing wang Xiaodong wang liansheng State Key Laboratory of Pollution Control and Resource Reuse School of Environment Nanjing University. Nanjing 210093 China College of Chemistry and Environment Nanjing Normal University Nanjing 210093 China

The molecular electronegativity-distance vector (MEDV) is employed to describe the chemical structure of bisphenol A analogs and their correlated estrogen activities. The result shows that the constructed models have ... 详细信息

关键词：双酚A类似物内分泌紊乱化学制品分子负电性距离向量雌激素定量构效关系

QSAR study on the non-monotonic dose-response curve of PCBs in chicken embryo hepatocyte bioassay

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Science China Chemistry 2009年第5期52卷 662-669页

作者： YunSong Mu AiQian Zhang ChangAn Gao SuFen Peng liansheng wang State Key Laboratory of Pollution Control and Resource Reuse School of Environment Nanjing University Nanjing 210093 China

Endocrine disrupting chemicals (EDCs) in the natural environment exhibit a unique non-monotonic dose-response curve and it is impossible to select one simple index to characterize the bilogogical activity of these compounds. Quantitative structure-activity relationship (QSAR) study on non-monotonic dose-response curve has become a real challenge presently. In order to explore the possible mechanism for the non-monotonic dose-response curve of polychlorinated biphenyls congeners (PCBs) in chicken embryo hepatocyte bioassay, AM1 method of ChemOffice was adopted to calculate necessary structure descriptors for PCBs, while the interactions between PCBs and simulated AhR ligand binding domain (LBD) were analyzed by using FlexX in SYBYL7.0. Different binding modes for PCBs have been distinguished not only from aligned conformation but also from free binding energy. Some QSAR models were established separately for both low and high doses ranges, revealing that receptor binding may predominate in the interference of the physiological function of cytochrome P4501A-P4501A in the low doses range. But with the higher doses range, the EROD suppression might be related to acute toxicity owing to molecular polarity or distribution of charges and consequently damage structure and function of chicken embryo hepatocyte.

关键词： polychlorinated biphenyls congeners (PCBs) non-monotonic dose-response curve quantitative structure-activity relationship (QSAR)

QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

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Science China Chemistry 2008年第7期51卷 677-683页

作者： JI Li wang XiaoDong LUO Si QIN Liang YANG XvShu LIU ShuShen wang liansheng State Key Laboratory of Pollution Control and Resources Reuse School of EnvironmentNanjing UniversityNanjing 210093China Key Laboratory of Yangtze River Water Environment Ministry of EducationCollege of Environmental Science and EngineeringTongji UniversityShanghai 200092China

Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a pow- erful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endo- crine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estro- gens (training set). Only nine descriptors calculated solely from the molecular structures of com- pounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The ob- tained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

关键词： quantitative structure-activity relationship estrogen receptor endocrine disruptors generalized regression neural network