Structural Characterization and Retention Time Prediction for Components of Essential Oil of Meconopsis Integrifolia Flowers

A molecular structural characterization (MSC) method called reduced molecular electronegativity-distance vector (MEDVR) was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression (SMR) and partial least square (PLS) methods, a model with the correlation coefficient (R1) of 0.987 and the standard deviation (SD1) of 1.377 could be obtained. Then through multiple linear regression (MLR), another model with the correlation coefficient (R2) of 0.989 and standard deviation (SD2) of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique (SMR), 8 vectors were selected to build up another model with its correlation coefficient (R3) and standard deviation (SD3) of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out (LOO) procedure, and the correlation coefficients (QCV) were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.     

酚类污染物土壤吸收系数定量预测

本文将分子结构表征方法(MEDV)进行改进,得到按非氢原子间距离分类的分子电性距离矢量,将该矢量用于酚类化合物结构表征,并与其土壤吸收系数(lgKoc)建立定量结构与性质关系(QSPR)模型。利用逐步回归(SMR)得到的4变量模型复相关系数(R2)为0.970、标准偏差(SD)为0.198,留一法(LOO)交互校验(CV)预测值的复相关系数(R2cv)为0.916、标准偏差(SDcv)为0.337。结果表明该矢量具有较强的分子结构表达能力,模型具有良好的预测能力与稳定性。     

三维全息原子场作用矢量用于吡咯类抗艾滋病药物QSAR研究

采用三维全息原子场作用矢量(3D-HoVAIF)对32个吡咯类抗艾滋病药物进行结构参数化表征,并与其活性建立定量构效关系。分别采用多元线性回归(MLR)和偏最小二乘(PLS)进行建模,建模的复相关系数(R2cum)、交互校验复相关系数(Q2cum)和模型的标准偏差(SD)分别为R2cum=0.914、Q2cum=0.812、SD=0.236(MLR);R2cum=0.836、Q2cum=0.719、SD=0.314(PLS),结果均优于文献值(R2cum=0.667,Q2cum=0.581,SD=0.420)。所建模型具有良好的稳定性和预测能力,表明3D-HoVAIF能够较好地表征该类分子的结构,值得进一步推广应用。     

Structural Characterization and Acute Toxicity Prediction of Substituted Aromatic Compounds by Using Molecular Vertexes Correlative Index

A molecular structural characterization (MSC) method called molecular vertexes correlative index (MVCI) was used to describe the structures of 30 substituted aromatic compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-toxicity relationship (QSTR) model with 4 variables was obtained. The correlation coefficient (R) of the model was 0.9467. Through partial least-squares regression (PLS), another QSTR model with 5 principal components was obtained. The correlation coefficient (R) of the model was 0.9518. Both models were evaluated by performing the cross-validation with the leave-one-out (LOO) procedure and the Cross-Validation (CV) correlation coefficients (RCV) were 0.9208 and 0.9214, respectively. The results suggested good stability and predictability of the models, and the molecular vertexes correlative index could successfully describe the structures of the substituted aromatic compounds.     

Structural characterization and prediction of Kovats retention indices (RI) for alkylbenzene compounds

A new molecular structural characterization (MSC) method called the molecular vertex eigenvalue correlative index (MVECI) is constructed and used to describe the structures of 122 alkylbenzene compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with correlation coefficient (R) of 0.995 is obtained. Through partial least-square regression (PLS), another QSRR model with correlation coefficient (R) of 0.991 is obtained. The estimation stability and prediction ability of the two models are strictly analyzed by both internal and external validations. For the internal validation, the cross-validation (CV) correlation coefficients (R _CV) of the two models are 0.993 and 0.988. For the external validation, the correlation coefficients (R _test) of the two models are 0.996 and 0.995, respectively. The results show that the stability and predictability of the models are good, and the molecular vertex eigenvalue correlative index can successfully describe the structures of alkylbenzene compounds.     

A QSRR Study on the Relative Retention Time of Halogenated Methyl-phenyl Ethers

1 INTRODUCTION Halogenated methyl-phenyl ethers (anisoles) are ubiquitous organic compounds in the environment. There are 134 possible congeners consisting of 19 chloroanisoles, 19 bromoanisoles and 96 bromo- chloroanisoles. Although they are not produced…     

Structural Characterization of Volatile Components of Rosa Banksiae Ait for Estimation and Prediction of Their Linear Retention Indices and Retention Times

The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.     

多溴代芴热力学性质的理论预测研究

为了预测环境污染物多溴代芴的热力学性质,计算了60种多溴代芴的分子连接性指数、电性距离矢量指数、电性拓扑状态指数以及分子形状指数.采用多元线性回归的方法建立了多溴代芴化合物的标准生成焓(Δ_fH~0)、标准熵(S~0)、标准生成吉布斯自由能(Δ_fG~0)及等容热容等热力学性质的QSPR相关性模型,方程的相关系数值均在0.99以上.与相关文献的相关系数R和标准误差S进行比较,该模型具有良好的预测能力和稳定性.     

抗艾滋病药物环尿素类化合物的结构表征及其生物活性的定量预测

提出一种新的结构描述子-按氢分类的电距矢量（H-MEDV），并用于环尿素类 化合物抗人类免疫缺陷病毒（human immuno-deficiency virus，简称HIV）活性预 测，籍以多元线性回归（MLR）建立了H-MEDV与活性之间的相关模型，取得了良好 的结果，相关系数达R = 0.971。另外采用逐步回归（SMR）从原模型参数中选取了 5个参数建立一新模型，其模型相关系数为R = 0.938；继以留一法（Leave-one- out，LOO）进行交互检验，相关系数与之接近，R = 0.908；说明了定量结构活性 相关模型具有很好的稳定性和预测能力。     

三维全息原子场作用矢量用于HEPT类抗艾滋病药物的QSAR研究

采用本实验室新近提出的三维全息原子场作用矢量表征34个1-[(2-羟乙氧)甲基]-6-苯硫基胸腺嘧啶(HEPT)类抗艾滋病药物结构并与其活性建立定量构效关系模型. 采用逐步回归对变量进行筛选后, 运用多元线性回归(multiple linear regression, MLR)建模的复相关系数(R2cum)、交互校验的复相关系数(Q2cum)和模型的标准偏差(SD)分别为R2cum=0.928、Q2cum=0.883与SD=0.43, 均优于Hancsh报道的值(R2cum=0.911、Q2cum=0.863与SD=0.45). 模型具有良好的稳定性和预测能力, 表明三维全息原子场作用矢量能较好表征该类分子结构信息, 值得进一步推广应用.     

一种新三维描述子用于Nevirapine类抗艾滋病药物定量构效关系的研究

运用三维全息原子场作用矢量(3D-HoVAIF)对33个Nevirapine类抗艾滋病药物进行了定量构效关系(QSAR)研究。采用偏最小二乘回归(PLSR)建立定量构效关系模型,同时采用内部及外部双重验证的方法对所得模型稳定性能进行深入分析和检验,所建模型的复相关系数(Rcum2)、留一法(LOO)交互校验(CV)复相关系数(Qcum2)和外部样本校验复相关系数(Qext2)分别为0·835、0·530和0·518。结果表明,3D-HoVAIF能较好表征Nevirapine类抗艾滋病药物分子结构信息,且所建模型具有较好稳定性能和预测能力。     

分子电性距离矢量用于稠环芳烃液相色谱保留值的QSPR研究

采用分子电性距离矢量(Molecular Electronegativity Distance Vector,MEDV)表征稠环芳烃类化合物的分子结构.分别运用多元线性回归(Multiple Linear Regres-sion,MLR)和偏最小二乘回归(PLS)建立了稠环芳烃类化合物结构与其液相色谱(LC)保留值的定量结构一性质关系(QSPR)模型,同时采用内部及外部双重验证的办法对所建模型稳定性能进行分析和验证,建模计算值、留一法交互检验预测值和外部样本预测值的复相关系数Rcum、RLOO、Qext分别为0.9970,0.9950,0.9925(MLR);0.9930,0.9790,0.9917(PLS).结果表明,MEDV能较好地表征该类分子结构信息,所建QSPR模型具有良好的稳定性和预测能力.为稠环芳烃类化合物分离、纯化、检测等方法的建立,提供有效的理论依据.     

含氟取代苯类化合物定量结构与毒性关系研究

本文采用了按氢分类的分子电距矢量(H-MEDV)对19个含氟取代苯类化合物进行了结构表征。采用多元线性回归(MLR)方法建立了含氟取代苯类化合物的定量结构-毒性关系(QSAR)的5变量模型;上述模型对19个含氟取代苯类化合物毒性预测与实验值较好的吻合,其复相关系数(R)为0.918,标准偏差(SD)为0.382,结果表明所建模型具有较好的稳定性和预测能力。     

无环醇~(13)C NMR化学位移与其结构参数的定量关系

用新颖的原子拓扑矢量Y_C、原子平衡电负性q_e、结构信息参数[N_H~i(i=α,β)]和γ校正参数对63个无环饱和脂肪醇的局部化学微环境进行了结构表征,并对化合物~(13)C NMR化学位移进行了QSSR研究.采用偏最小二乘回归得到模型的复相关系数R和标准偏差S分别为0.9915和2.4827;对353个碳原子~(13)C NMR化学位移的实验值与计算值的平均绝对误差仅为2.01×10~(-6).同时,采用留分法(Leave-molecule-out)和外检验方法测试模型的内部稳定性和外部预测能力.与文献结果比较,本研究所用参数少,且计算简便.     

脂肪酸多聚甘油酯分子结构与性能相关性研究

采用量子化学MM+和AM1方法计算聚甘油和脂肪酸多聚甘油酯的分子结构参数,然后用逐步线性回归方法建立脂肪酸多聚甘油酯HLB值的定量结构性质(QSPR)模型,所得的预测模型中包含四个参数(单位质量分子所含氧原子数Xo、生成热ΔfHm、电子能Ee和水合能Eh),预测值及外部检验的复相关系数(R2)和标准偏差(SD)分别为0.9553、0.73722和0.9678、6.34426。结果表明,量子化学方法计算简单,对脂肪酸多聚甘油酯结构的表征能力较强,所建QSPR模型具有能较好的预测能力和较强的稳健性,并在一定程度上阐明了脂肪酸多聚甘油酯分子结构与性能之间的关系。     

三维全息原子场作用矢量用于猫爪草脂肪酸的构效关系研究

运用三维全息原子场作用矢量(3D-HoVAIF)对猫爪草中18种脂肪酸进行定量构效关系(QSAR)研究。采用逐步回归(stepwise multiple regression,SMR)进行变量筛选,偏最小二乘回归(partial least square regression,PLS)建立定量构效关系模型。所建模型复相关系数(R2cum)、留一法交互校验(CV)复相关系数(Qc2um)分别为0.977和0.946。结果表明,3D-HoVAIF能较好表征猫爪草中脂肪酸的结构信息,且所建模型具有较好稳定性和预测能力。