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 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.     

Structural Characterization and Toxicity Prediction of Some Organic Compounds

A new molecular structural characterization(MSC)method called molecular vertexes correlative index(MVCI)was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitative structure-activity relationship(QSAR)model of toxicity(–lgEC50)was obtained through multiple linear regression(MLR)and stepwise multiple regression(SMR).The correlation coefficient(R)of the model was 0.912,and the standard deviation(SD)of the model was 0.525.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The Leave-One-Out(LOO)Cross-Validation(CV)correlation coefficient(RCV)was 0.816 and the standard deviation(SDCV)was 0.739,respectively.For the external validation,the correlation coefficient(Rtest)was 0.905 and the standard deviation(SDtest)was 0.520,respectively.The results showed that the index was superior in molecular structural representation.The stability and predictability of the model were good.     

Study on the Quantitative Structure-toxicity Relationships for the Selected Esters by Using Molecular Electronegativity Interaction Vector （MEIV）

The molecular electronegativity interaction vector (MEIV) was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression (MLR) and partial least-squares regression (PLS). The correlation coefficients (R) of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The cross-verification correlation coefficients (RCV) of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.     

分子电性距离矢量用于多溴联苯醚分子结构表达及色谱保留时间预测

应用分子电性距离矢量(MEDV)对多溴联苯醚(PBDEs)的209种同系物进行结构表征.通过多元线性回归的方法,建立了PBDEs定量结构-色谱保留(QSRR)关系的6个变量和5个变量的两种模型.两种模型的建模计算值复相关系数R均为0.995;用留一法(LOO)进行了交互检验,其复相关系数(R2cv)分别为0.987和0...     

Estimation of n-Octanol/water Partition Coefficients （lgKow） and the Aqueous Solubility （-lgSw） of all PCDF Congeners by Density Functional Theory

1 INTRODUCTION As is widely known, PCDFs together with poly- chlorinated dibenzo-p-dioxins (PCDDs) and poly- chlorinated biphenyls (PCBs) have been included in many blacklists, such as “persistent organic pol- lutants (POPs)” and suspected “environmental en- docrine disruptors (EEDs)”. This is because PC- DFs are chemically and thermally stable and com- plex PCDF mixtures are generally resistant to bio- degradation. Although the concentration of these chemicals is quite low o…     

Toxicity （-lgEC50） Measurement of the Fluorobenzene Derivants against Vibrio Qinghaiensis （Q67） and Their 2D,3D-QSAR Study

Toxicities (-lgEC50) of 16 fluorobenzene derivants against vibrio qinghaiensis (Q67) were measured systematically,and their quantum chemistry parameters were calculated at the B3LYP/6-311G** level. Based on the experimental toxicity data and quantum chemistry parameters,2D-QSAR model was proposed,which was validated by variance inflation factors (VIF),t-value and cross-validation method. At the mean time,comparative molecular force field (CoMFA) based on molecular simulation was used to investigate the toxicity of fluorobenzene derivants. Furthermore,the intoxicating mechanism of fluorobenzene derivants was discussed. To our interest,2D-QSAR and CoMFA models exhibit good prediction ability,with which the toxicity of similar compounds can be predicted. Finally,toxicities (-lgEC50) of 12 fluorobenzene derivants against vibrio qinghaiensis (Q67) were predicted with these models.     

含磷嘧啶类CDK9抑制剂的分子对接、3D-QSAR和分子动力学模拟

选取64个具有潜力的含磷嘧啶类细胞周期依赖性蛋白激酶(CDK9)小分子抑制剂,采用分子对接方法研究了该类小分子与CDK9的结合作用,结果表明,分子构象、氢键形成、疏水性和氨基酸残基Cys106在此类抑制剂与CDK9的结合过程中具有重要作用.在配体叠合的基础上,运用比较分子力场分析(Co MFA)、比较分子相似性指数分析(Co MSIA)和Topomer Co MFA(T-COMFA)研究了分子结构与抑制活性的关系,发现由训练集立体场、静电场和疏水场组合的Co MSIA模型为最优模型,其内部交叉验证相关系数(Q2=0.557)、非交叉验证相关系数(R2=0.959)和外部预测相关系数(r2=0.863)具有统计学意义,该模型的三维等值线图直观显示了化合物的活性与其三维结构的关系.根据这些结果设计了10个具有新结构的含磷嘧啶类化合物,分子对接和分子动力学模拟结果表明,新化合物和CDK9的结合模式与原化合物64相同,自由能分析从理论上证明了新化合物64d的CDK9抑制活性优于化合物64,并且显示含磷基团与残基Asp109的静电场能在化合物与CDK9作用过程中有重要作用.     

分子电性距离矢量用于三嗪类化合物的构效关系研究

采用分子电性距离矢量(Molecular Electronegativity Distance Vector,MEDV)表征了三嗪类化合物的分子结构,并运用多元线性回归(Multiple Linear Regression,MLR)建立了该类化合物结构与其发光菌和大型蚤毒性的定量结构-毒性相关(Quanti-tative Structure-Toxicity Relationship,QSTR)模型,同时采用留一法交互检验对所建模型进行了分析和验证,建模计算值的相关系数R分别为0.970和0.952,留一法交互检验预测值的相关系数RLOO分别为0.917和0.921,并进一步阐述了结构与毒性之间的关系。结果表明,三嗪环上π电子离域程度减小有利于毒性增加,侧链N上取代基数目增加,化合物毒性减小。为进一步预测该类化合物的毒性,进行药物筛选提供了有效的理论依据。     

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.     

定量结构-性质相关原理在阴离子表面活性剂临界胶束浓度预测中的应用

利用定量结构-性能相关（QSPR）原理，建立起了8类不同结构，计40个阴离子 表面活性剂临界胶束浓度（cmc）的定量模型。所得到的最佳模型包括：分子总能 量（E_T）、分子生成热（ΔH_f）、分子偶极矩（D）、前线分子轨道能量（E_ (LUMO),E_(HOMO)）及憎水基0级Kier & Hall指数（KHO），计6个描述符，复相关 系数R~2 = 0.9778。