Quantitative Structure-retention Relationship Study of Polychlorinated Dibenzothiophenes by Molecular Electronegativity Distance Vector(MEDV)

Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.

QSAR Studies on the Inhibitory Activityof Levofloxacin-thiadiazole HDACi Conjugates to Histone Deacetylases

A molecular electronegativity distance vector(M)based on 13 atomic types has been used to describe the structures of 19 conjugates(LHCc)of levofloxacin-thiadiazole HDAC inhibitor(HDACi)and related inhibitory activities(pH,i=1,2,6)of LHCc against histone deacetylases(HDACs,such as HDAC1,HDAC2 and HDAC6).The quantitative structure-activity relationships(QSAR)were established by using leaps-and-bounds regression analysis for the inhibitory activities(pH)of 19 above compounds to HDAC1,HDAC2 and HDAC6 along with M.The correlation coefficients(R~2)and the leave-one-out(LOO)cross validation Rfor the pH,pHand pHmodels were 0.976 and 0.949;0.985 and 0.977;0.976 and 0.932,respectively.The QSAR models had favorable correlations,as well as robustness and good prediction capability by R~2,F,R~2,A,Fand Vtests.Validated by using 3876 training sets,the models have good external prediction ability.The results indicate that the molecular structural units:–CH–(g=1,2),–NH,–OH,=O,–O–and–S–are the main factors which can affect the inhibitory activity of pH,pHas well as pHbioactivities of these compounds directly.Accordingly,the main interactions between HDACs inhibitor and HDACs are hydrophobic interaction,hydrogen bond,and coordination with Znto form compounds,which is consistent with the results in reports.

QSPR of n-Octanol/water Partition Coefficient (lgK_(ow)) for Alkyl(1-phenylsulfonyl) Cycloalkane-carboxylates

Quantum chemistry parameters of 28 alkyl（1-phenylsulfonyl） cycloalkane-carboxy-lates were computed at the 6-31G＊ level in fully optimal manner using B3LYP method of density functional theory （DFT）. With GQSARF2.0 program, the correlation equations that can predict n-octanol/water partition coefficient （lgKow） were developed using the structural and thermodynamic parameters of 28 alkyl（1-phenylsulfonyl） cycloalkane-carboxylates with experimental data of lgKow as theoretical descriptors; the correlation coefficient （R^2） was 0.9452 and the cross-validation squared correlation coefficient （Rcv^2） 0.9312. Furthermore, a four-variable model from MEDV was obtained, of which R2 = 0.9497 and Rov^2 =0.9388. The models were validated by variance inflation factor （VIF） and t-test. Cross-validation indicates that the correlation and predicting ability of the model based on both DFT method and MEDV are more advantageous than those obtained from semi-empirical AM1 method.

On QSAR Study of Stereoselectivity for Wittig Reaction

Molecular structures of reactants were characterized by molecular electronegativity distance vector （VHMED） considering hydrogen association. A reasonable molecular modeling equation with 4-parameters was achieved for quantitative structure-property/activity relationship （QSPR/QSAR） by stepwise multiple regression （SMR） that the variable was introduced item by item in significant level order. A high correlation coefficient （R = 0.980） demonstrates that the model is able to well express a quantitative relation between stereoselectivity and the reactant structures as quantitative structure-reactivity/stereoselectivity relationship （QSRR/QSSR）. The multiple correlation coefficient （Rcv= 0.964） was tested through cross-validation with the leave-one-out （LOO） procedure. The above results show that the model possesses high estimation stability and good prediction ability between the amount of both cis and trans isomers in products and reactants.

Predicting the Relative Retention Time (RRT) of Polybrominated Diphenyl Ethers (PBDEs)

Using the molecular electronegativity distance vector descriptors derived directly from the molecular topological structures, the relative retention time （RRT） of polybrominated diphenyl ethers （PBDEs） were predicted. A four-variable regression model （M30） with the correlation coefficient of 0.9816 and the root mean square errors of 0.061 was developed using a training set including 30 PBDEs. The correlation coefficient of 0.9841 and the root mean square errors of 0.054 between the values of RRT predicted by M30 and the RRT observed for 16 external PBDEs show a good predictive potential of M30. The descriptors included in the M30 represent four interactions between four pairs of atom types, i.e., atom -C= and -C=, -C= and 〉C=, 〉C= and 〉C=, -C= and -Br.

MEDV-13 for QSRR of 62 Polychlorinated Naphthalenes

A molecular electronegativity distance vector based on 13 atomic types (MEDV-13) is used to describe the structures of 62 polychlorinated naphthalene (PCN) congeners and related to the gas chromatographic relative retention indices (RIs) of PCNs. Using multiple linear regression, a 4-variable quantitative structure-retention relationship (QSRR) with the correlation coefficient of estimations (r) being 0.9912 and the root mean square error of estimations (RMSEE) being 31.4 and the correlation coefficient of predictions (q) and the root mean square error of predictions (RMSEP) in the leave-one-out procedure are 0.9898 and 33.76, respectively.

Study on the Quantitative Relationship between the Structure and Toxicity of Organophosphorus Pesticide

The molecular electronegativity distance vector（MEDV） was applied to characterize the molecular structures of 30 organophosphorous compounds. Optimum MEDV descriptors were selected by using the variable selection and modeling method based on the prediction（VSMP） technique. The quantitative structure-toxicity relationship（QSTR） model was built for acute toxicity（96h pLC50） of organophosphorous compounds to steelhead. The developed QSTR model with strictly internal and external validations presents relatively high correlation coefficient（R2） of 0.9518, leave-one-out（LOO） cross-validated correlation coefficient（Q2LOO） of 0.9355, and leave-many-out（LMO） cross-validated correlation coefficient（Q2LMO） of 0.9290. The robustness of the model was confirmed by the y-randomization test（R2yrand = 0.0772 and Q2 yrand = –0.5313） and bootstrapping（R2bstr = 0.9502 and Q2 bstr = 0.9177） method. The result of external validation, Q2F1 = 0.9336, Q2F2 = 0.9336, Q2F3 = 0.9447, r2 m = 0.8120, and CCC = 0.9602, shows that the QSTR model has a high predictive ability.

Prediction of the Aqueous Solubilities of Polychlorinated Biphenyls

Using the molecular electronegativity distance vector descriptors derived directly from the molecular topological structures, the aqueous solubilities of polychlorinated biphenyls (PCBs) were predicted. A three-variable regression equation with correlation coefficient of 0.9739 and the root mean square errors of 0.26 was developed. The descriptors included in the equation represent three interactions between three pairs of atomic types, i.e., atom -C= and >C=, -C= and -Cl, and -Cl and -Cl. It has been proved that the aqueous solubilities of 137 PCB congeners can be accurately predicted as long as there are more than 65 calibration compounds.

An atomic electronegative distance vector and carbon-13 nuclear magnetic resonance chemical shifts of alcohols and alkanes

A novel atomic electronegative distance vector (AEDV) has been developed to express the chemical environment of various chemically equivalent carbon atoms in alcohols and alkanes. Combining AEDV and γ parameter, four five-parameter linear relationship equations of chemical shift for four types of carbon atom are created by using multiple linear regression. Correlation coefficients are R = 0.9887, 0.9972, 0.9918 and 0.9968 end roots of mean square error are RMS = 0.906, 0. 821, 1.091 and 1.091 of four types of carbons, i.e., type 1, 2, 3, and 4 for primary, secondary, tertiary, and quaternary carbons, respectively. The stability and prediction capacity for external samples of four models have been tested by cross-validation.

QSAR Studies on the COX-2 Inhibition by 3,4-Diarylcycloxazolones Based on MEDV Descriptor

Selective inhibition of cyclooxygenase-2 (COX-2) might avoid the side effects of current available nonsteroidal antiinflammatory drugs while retaining their therapeutic efficacy. A novel variable selection and modeling method based on prediction is developed to construct the quantitative structure-activity relationships (QSAR) between the molecular electronegativity distance vector (MEDV) based on 13 atomic types and the biological activities of a set of selective cyclooxygenase-2 inhibitory molecules,3,4-diarylcycloxazolones (DAA) plus indomethacin,naproxen,and celecoxib. Using multiple linear regression,a 5-variable linear model is developed with the calibrated correlation coefficient of 0.9271 and root mean square error of 0.17 in modeling stage and the validated correlation coefficient of 0.9030 and root mean square error of 0.20 in leave-one-out validation step,respectively. To further test the predictive ability of the model,20 DAA compounds are picked up to construct a training set which is used to build a QSAR model and then the model is employed to predict the biological activities of the balance compounds. The predicted correlation coefficient and root mean square error are 0.9332 and 0.19, respectively.