Quantitative Structure Activity Relationship Studies of Benzoxazinone Derivative Antithrombotic Drug Using New Three-dimensional Structure Descriptors

A novel three-dimensional holographic vector of atomic interaction field(3D-HoVAIF) was used to describe the chemical structures of 23 benzoxazinone derivatives as antithrombotic drugs.Here a quantitative structure activity relationship(QSAR) model was built by partial least-squares(PLS) regression.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The correlation coefficients of established PLS model,leave-one-out(LOO) cross-validation,and predicted values versus experimental ones of external samples were R2=0.899,RCV2=0.854 and Qext2=0.868,respectively.These values indicated that the built PLS model had both favorable estimation stability and good prediction capabilities.Furthermore,the satisfactory results showed that 3D-HoVAIF could preferably express the information related to the biological activity of benzoxazinone derivatives.     

QSAR Study on Some N-[5-（2-Furanyl）-2-methyl- 4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl]-carboxamide and 3-Substituted-5-（2-furanyl）-2-methyl-3H-thieno- [2,3-d]pyrimidin-4-ones Using Three-dimensional Holographic Vector of Atomic Interaction Field

Study on the quantitative structure-activity relationship （QSAR） of 26 compounds, N-[5-（2-furanyl）-2-methyl-4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl]-carboxamide and 3-substituted- 5-（2-furanyl）-2-methyl-3H-thieno[2,3-d]pyrimidin-4-ones, with three-dimensional holographic vector of atomic interaction field （3D-HoVAIF） was carried out. SMR-PLS QSAR models have been created and good correlation coefficients and cross-validated correlation coefficients were obtained. The result shows that the models have good prediction capability and favorable stability and the 3D-HoVAIF is applicable to the molecular structural characterization and biological activity prediction.     

A QSAR study and molecular design of benzothiazole derivatives as potent anticancer agents

A quantitative structure-activity relationship (QSAR) of a series of benzothiazole derivatives showing a potent and selective cytotoxicity against a tumorigenic cell line has been studied by using the density functional theory (DFT), molecular mechanics (MM ) and statistical methods, and the QSAR equation was established via a correlation analysis and a stepwise regression analysis. A new scheme determining outliers by "leave-one-out" (LOO) cross-validation coefficient (q2n-i) was suggested and successfully used. In the established optimal equation (excluding two outliers), the steric parameter (MRR) and the net charge (QFR) of the first atom of the substituent (R), as well as the square of hydrophobic parameter (lgP)2 of the whole molecule, are the main independent factors contributing to the anticancer activities of the compounds. The fitting correlation coefficient (R2) and the cross-validation coefficient (q2) values are 0.883 and 0.797, respectively. It indicates that this model has a significantly statistical quality and an excellent prediction ability. Based on the QSAR studies, 4 new compounds with high predicted anticancer activities have been theoretically designed and they are expected to be confirmed experimentally.     

Molecular Docking and 3D-QSAR Research of Biphenyl Carboxylic Acid MMP-3 Inhibitors

The molecular docking by LigandFit docking of Discovery Studios 2.5 was employed to the three-dimensional quantitative structure-activity relationship(3D-QSAR) studies of biphenyl carboxylic acid MMP3 inhibitors.A significant correlation coefficient was obtained between dock scores and biological activities.Based on the optimal docking conformations,3D-HoVAIF was employed to the QSAR studies of 51 biphenyl carboxylic acid MMP-3 inhibitors.R2 and Q_CV2(leave-one-out,LOO) of the optimal 3D-HoVAIF-PLS model were 0.873 and 0.841 respectively.The conclusions obtained from the PLS analysis were in agreement with the docking results.     

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.     

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.     

Quantitative Analysis of Catalpol in Chinese Patent Medicine Lixin Pill by Near-infrared Diffuse Reflectance Spectroscopy

Lixin pill is a typical Chinese patent medicine with anti-rheumatic heart disease activity that has been widely used in clinical practice. Therefore it is very important to detect the concentration of catalpol, as the main component of the active ingredient. Near-infrared reflectance（NIR） spectroscopy was used to study the content of catalpol in the unprocessed Chinese patent medicine of Lixin pills. NIR is applied to quantitatively analyze 77 sam- ples, which were randomly divided into a calibration set containing 61 samples and a prediction set containing 16 samples. To get a satisfying result, partial least squares（PLS） regression was utilized to establish quantitative models. In PLS regression, the values of coefficient of determination（R2） and root mean square error of cross-validation （RMSECV） of PLS regression are 0.9419 and 0.0216, respectively. The process of establishing model, parameters of model, and prediction results were also discussed in detail（root mean square error of prediction is 0.0164）. The over- all results show that NIR spectroscopy can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in the Chinese patent medicine of Lixin pills. The prediction set suggests that this quantitative analysis model has excellent generalization ability and prediction precision. Accordingly, the result can provide tech- nical support for the further analysis of catalpol in unprocessed Lixin pill. Moreover, this study supplied technical support for the further analysis of other Chinese patent medicine samples.     

Studies on the QSAR of ACE Inhibitory Tripeptides with Proline as C-Terminal and Determination Inhibitory Activities

A database of 38 tripeptides with C-terminal proline was constructed to study QSAR of ACE inhibitory peptides. The model was established using PLS based on the three z-scores of 20 coded amino acids. The prediction ability of tripeptides model was improved with proline as terminal. The coefficient of determination(R2) and the cross validated coefficient(Q2LOO) of the model are 0.856 and 0.782 respectively. According to the model, two potential ACE inhibitory tripeptides IIP and IVP were synthesized. Their IC50(inhibitor concentration that reduced enzyme activity by 50%) were proved to be 1.58 and 1.39 μM respectively by direct spectrophotometric measurement, and they are very close to the predicted value by the model.     

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.     

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.     

2D-QSARs of 1-Alkoxymethyl-5-alkyl-6-naphthylmethyl Uracils as HEPT Analogues with Anti-HIV-1 Activity

The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum chemical parameters and physicochemical parameters. The best model of three parameters yields r = 0.908, r^2A = 0.800 and s = 0.467 based on stepwise multiple regression （SMR） method. The stability of the model has been verified by t-test, and the results show that the model has perfect robustness. The predictive power of QSAR models has been tested by Leave-One-Out （LOO） and Leave-Group（regularly random set）-Out（LGO） procedure Cross-Validation methodology. The r^2cv of 0.755 and r^2pred of 0.759 were obtained, respectively.     

Construction of the Pharmacophore Model of Glycogen Synthase Kinase-3 Inhibitors

A three dimensional pharmacophore model has been generated for glycogen synthase kinase-3 （GSK-3） inhibitots. A dataset consisting of 89 compounds was selected on the basis of the information content of the structures and activity data as required by the CATALYST system. The optimum model with four features （one hydrogen-bond acceptor unit, one ring aromatic unit, and two hydrophobic aromatic units） was selected with a good correlation coefficient （0.95）. This model is able to predict the activity of other known GSK-3 inhibitors not included in the model generation, and can be further used to identify structurally diverse compounds with desired biological activity by virtual screening.     

CoMFA 3D-QSAR Analysis of Epothilones Based on Docking Conformation and Alignment

Epothilones belong to a class of novel microtubule stabilizing and anti-mitotic agents, which have a paclitaxel-like mechanism of action. A three-dimensional quantitative structure-activity relationship （3D-QSAR） model was built for epothilones by the method of comparative molecular field analysis （CoMFA） combined with the flexible docking technology. The docking CoMFA model gave a good cross-validated value of q2=0.784 with an optimized component of 6 and the conventional correlation coefficient of r^2=0.985. The statistical results show that the model has good ability to predict the activity of the studied compounds. At last, the docking CoMFA model was analyzed through contour maps complemented with MOLCAD-generated active site potential surface in the α,β-tubulin receptor, which can provide important information for the structure-based drug design.     

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

Polychlorinated biphenyls(PCBs) can antagonize human pregnane X receptor(hPXR) activation.Such chemicals could pose a serious threat to the reproductive and developmental ability of humans.The quantitative structure activity relationship(QSAR) provides a promising method for the estimation of PCBs’ antagonistic activity.In 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 sets.The 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 ability.The applicability domain(AD) of the model was assessed by Williams plot.The antagonistic activity(?logKi) of 108 PCBs,which are unavailable by experiment at present,was predicted within the applicability domain of the model.The critical structural features related to the activity of PCBs were identified.