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 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.     

QSRR Study on the Components of Styrax Japonicus Sieb Flowers Using Improved Molecular Electronegativity-distance Vector （I-MEDV）

Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization(MSC) method called improved molecular electronegativity-distance vector(I-MEDV) was developed. It was used to describe the structures of 37 compounds of styrax japonicus sieb flowers. Through multiple linear regression(MLR),a QSRR model was built up. The correlation coefficient(R1) of the model was 0.980. Then,4 vectors were selected to build another model through the method of stepwise multiple regression(SMR) ,and the correlation coefficient(R2) of the model was 0.975. Moreover,all the two models were evaluated by performing the crossvalidation with the leave-one-out(LOO) procedure and the correlation coefficients(Rcv) were 0.948 and 0.968,respectively. The results show that the I-MEDV could successfully describe the structures of organic compounds. The stability and predictability of the models were good.     

Quantitative Structure Property Relations （QSPR） for Predicting Molar Diamagnetic Susceptibilities,xm, of Inorganic Compounds

For predicting the molar diamagnetic susceptibilities of inorganic compounds, a novel connectivity index ^mG based on adjacency matrix of molecular graphs and ionic parameter gi was proposed. The gi is defined as gi=（ni^0.5-0.91）^4·xi^0.5｜Zi^0.5, where Zi, ni, xi are the valence, the outer electronic shell primary quantum number, and the electronegativity of atom i respectively. The good QSPR models for the molar diamagnetic susceptibilities can be constructed from ^0G and ^1G by using multivariate linear regression （MLR） method and artificial neural network （NN） method. The correlation coefficient r, standard error, and average absolute deviation of the MLR model and NN model are 0.9868, 5.47 cgs, 4.33 cgs, 0.9885, 5.09 cgs and 4.06 cgs, respectively, for the 144 inorganic compounds. The cross-validation by using the leave-one-out method demonstrates that the MLR model is highly reliable from the point of view of statistics. The average absolute deviations of predicted values of the molar diamagnetic susceptibility of other 62 inorganic compounds （test set） are 4.72 cgs and 4.06 cgs for the MLR model and NN model. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an inorganic compound. Both MLR and NN methods can provide acceptable models for the prediction of the molar diamagnetic susceptibilities. The NN model for the molar diamagnetic susceptibilities appears more reliable than the MLR model.