The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivative...The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors： net atomic charge on the atom N connecting with R, dipole moment and heat of formation, By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation （LOOCV） test, The results indicate that the performance of the SVC model can exceed that of the principal component analysis （PCA） and K-Nearest Neighbor （KNN） models for this real world data set.展开更多
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 struc...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.展开更多
In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a b...In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.展开更多
The new orthogonal relationship is generalized for orthotropic elasticity of three-dimensions. The thought of how dual vectors are constructed in a new orthogonal relationship for theory of elasticity is generalized i...The new orthogonal relationship is generalized for orthotropic elasticity of three-dimensions. The thought of how dual vectors are constructed in a new orthogonal relationship for theory of elasticity is generalized into orthotropic problems. A new dual vector is presented by the dual vector of the symplectic systematic methodology for elasticity that is over again sorted. A dual differential equation is directly obtained by using a mixed variables method. A dual differential matrix to be derived possesses a peculiarity of which principal diagonal sub-matrixes are zero matrixes. As a result of the peculiarity of the dual differential matrix, two independently and symmetrically orthogonal sub-relationships are discovered for orthotropic elasticity of three-dimensions. The dual differential equation is solved by a method of separation of variable. Based on the integral form of orthotropic elasticity a new orthogonal relationship is proved by using some identical equations. The new orthogonal relationship not only includes the symplectic orthogonal relationship but is also simpler. The physical significance of the new orthogonal relationship is the symmetry representation about an axis z for solutions of the dual equation. The symplectic orthogonal relationship is a generalized relationship but it may be appeared in a strong form with narrow sense in certain condition. This theoretical achievement will provide new effective tools for the research on analytical and finite element solutions to orthotropic elasticity of three-dimensions.展开更多
Considering atomic property vector and atomic correlative function, the 3-dimensional structural vector of atomic property correlation （3D-VAPC）, a novel descriptor,is defined to characterize a 3-dimensional molecul...Considering atomic property vector and atomic correlative function, the 3-dimensional structural vector of atomic property correlation （3D-VAPC）, a novel descriptor,is defined to characterize a 3-dimensional molecular structure by introducing self-adaptability regulation mechanism and the idea of orientating to customers. Characterizing the structures of 25 bisphenol A compounds by this vector, the QSAR models of three kinds of estrogen activities （ER affinities, gene induction and cell proliferation） have high multiple correlation coefficient （Rcum^2=0.933, 0.813, 0.959） and cross verification coefficient （Qcum^2=0.847, 0.953, 0.798） by support vector machine （SVM）, which suits for nonlinear circumstances. The above results show that the models successfully express the correlation between structure and three kinds of estrogen activities. Therefore, 3D-VAPC exactly reflects the molecular structural information and SVM method correctly describes the correlation between information and property of the compounds.展开更多
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 molecu...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.展开更多
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...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.展开更多
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 ...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.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant Nos.20373040, 20503015）
文摘The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors： net atomic charge on the atom N connecting with R, dipole moment and heat of formation, By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation （LOOCV） test, The results indicate that the performance of the SVC model can exceed that of the principal component analysis （PCA） and K-Nearest Neighbor （KNN） models for this real world data set.
基金supported by the Foundation of Returned Scholars (Main Program) of Shanxi Province (200902)
文摘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.
基金Project supported by National Natural Science Foundation of China（ Grant No. 20373040）
文摘In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.
文摘The new orthogonal relationship is generalized for orthotropic elasticity of three-dimensions. The thought of how dual vectors are constructed in a new orthogonal relationship for theory of elasticity is generalized into orthotropic problems. A new dual vector is presented by the dual vector of the symplectic systematic methodology for elasticity that is over again sorted. A dual differential equation is directly obtained by using a mixed variables method. A dual differential matrix to be derived possesses a peculiarity of which principal diagonal sub-matrixes are zero matrixes. As a result of the peculiarity of the dual differential matrix, two independently and symmetrically orthogonal sub-relationships are discovered for orthotropic elasticity of three-dimensions. The dual differential equation is solved by a method of separation of variable. Based on the integral form of orthotropic elasticity a new orthogonal relationship is proved by using some identical equations. The new orthogonal relationship not only includes the symplectic orthogonal relationship but is also simpler. The physical significance of the new orthogonal relationship is the symmetry representation about an axis z for solutions of the dual equation. The symplectic orthogonal relationship is a generalized relationship but it may be appeared in a strong form with narrow sense in certain condition. This theoretical achievement will provide new effective tools for the research on analytical and finite element solutions to orthotropic elasticity of three-dimensions.
基金This work was supported by the Natural Science Foundation of CQ CSTC (No. 2006BB5177)
文摘Considering atomic property vector and atomic correlative function, the 3-dimensional structural vector of atomic property correlation （3D-VAPC）, a novel descriptor,is defined to characterize a 3-dimensional molecular structure by introducing self-adaptability regulation mechanism and the idea of orientating to customers. Characterizing the structures of 25 bisphenol A compounds by this vector, the QSAR models of three kinds of estrogen activities （ER affinities, gene induction and cell proliferation） have high multiple correlation coefficient （Rcum^2=0.933, 0.813, 0.959） and cross verification coefficient （Qcum^2=0.847, 0.953, 0.798） by support vector machine （SVM）, which suits for nonlinear circumstances. The above results show that the models successfully express the correlation between structure and three kinds of estrogen activities. Therefore, 3D-VAPC exactly reflects the molecular structural information and SVM method correctly describes the correlation between information and property of the compounds.
基金supported by the Youth Foundation of Education Bureau,Sichuan Province (09ZB036)Technology Bureau,Sichuan Province (2006j13-141)
文摘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.
基金Supported by the Fund of National High Technology Research and Development Program (863 Program, No. 2006AA02Z312)
文摘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.
基金supported by the Natural Science Foundation of Shaanxi Province (2009JQ2005)Foundation of Educational Commission of Shaanxi Province (09JK358) Graduate Innovation Fund of Shaanxi University of Science and Technology
文摘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.
