Three-dimensional quantitative structure activity relationship （3D-QSAR） and docking studies of a series of arylthioindole derivatives as tubulin inhibitors against human breast cancer cell line MCF-7 have been carr...Three-dimensional quantitative structure activity relationship （3D-QSAR） and docking studies of a series of arylthioindole derivatives as tubulin inhibitors against human breast cancer cell line MCF-7 have been carried out. An optimal 3D-QSAR model from the comparative molecular field analysis （CoMFA） for training set with significant statistical quality （R2=0.898） and predictive ability （q2=0.654） was established. The same model was further applied to predict pIC50 values of the compounds in test set, and the resulting predictive correlation coefficient R2（pred） reaches 0.816, further showing that this CoMFA model has high predictive ability. Moreover, the appropriate binding orientations and conformations of these compounds interacting with tubulin are located by docking study, and it is very interesting to find the consistency between the CoMFA field distribution and the 3D topology structure of active site of tubulin. Based on CoMFA along with docking results, some important factors improving the activities of these compounds were discussed in detail and were summarized as follows： the substituents R3-R5 （on the phenyl ring） with higher electronegativity, the substituent R6 with higher eleetropositivity and bigger bulk, the substituent R7 with smaller bulk, and so on. In addition, five new compounds with higher activities have been designed. Such results can offer useful theoretical references for experimental works.展开更多
In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using v...In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using various types of chromosomes is used.The first method is a GA with binary chromosome（GA-BC） and the other is a GA with a fixed-length character chromosome（GA-FCC）.The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression（FSR） model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability（R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675） using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.展开更多
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 quantitativ...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.展开更多
A quantitative structure–activity relationship(QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using ...A quantitative structure–activity relationship(QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using partial least squares regression coupled with stepwise forward–backward variable selection method. QSAR analyses were performed on the available IC50 D10 strains of Plasmodium falciparum data based on theoretical molecular descriptors. The QSAR model developed gave good predictive correlation coefficient(r2) of 0.8994, significant cross validated correlation coefficient(q2) of 0.7689, r2 for external test set)(2predr of 0.8256, coefficient of correlation of predicted data set)(2sepred,r of 0.3276. The model shows that antimalarial activity is greatly affected by donor and electron-withdrawing substituents. The study implicates that chalcone and dienone rings should have strong donor and electron-withdrawing substituents as they increase the activity of chalcone. Results show that the predictive ability of the model is satisfactory, and it can be used for designing similar group of antimalarial compounds. The findings derived from this analysis along with other molecular modeling studies will be helpful in designing of the new potent antimalarial activity of clinical utility.展开更多
The abatements of 89 pharmaceuticals in secondary effluent by ozonation and the electro-peroxone(E-peroxone)process were investigated.Based on the results,a quantitative structure-activity relationship(QSAR)model was ...The abatements of 89 pharmaceuticals in secondary effluent by ozonation and the electro-peroxone(E-peroxone)process were investigated.Based on the results,a quantitative structure-activity relationship(QSAR)model was developed to explore relationship between chemical structure of pharmaceuticals and their oxidation rates by ozone.The orthogonal projection to latent structure(OPLS)method was used to identify relevant chemical descriptors of the pharmaceuticals,from large number of descriptors,for model development.The resulting QSAR model,based on 44 molecular descriptors related to the ozone reactivity of the pharmaceuticals,showed high goodness of fit(R^(2)=0.963)and predictive power(Q^(2)=0.84).After validation,the model was used to predict second-order rate constants of 491 pharmaceuticals of special concern(k_(O_(3)))including the 89 studied experimentally.The predicted k_(O_(3))values and experimentally determined pseudo-first order rate constants of the pharmaceuticals’abatement during ozonation(k_(OZ))and the E-peroxone process(k_(EP))were then used to assess effects of switching from ozonation to the E-peroxone process on removal of these pharmaceuticals.The results indicate that the E-peroxone process could accelerate the abatement of pharmaceuticals with relatively low ozone reactivity(k_(O_(3))<∼10^(2)M^(−1)⋅s^(−1))than ozonation(3–10 min versus 5–20 min).The validated QSAR model predicted 66 pharmaceuticals to be highly O_(3)-resistant.The developed QSAR model may be used to estimate the ozone reactivity of pharmaceuticals of diverse chemistry and thus predict their fate in ozone-based processes.展开更多
Quantitative structure-activity relationships(QSARs)were determined using partial least square(PLS)and support vector machine(SVM).The predicted values by the final QSAR models were in good agreement with the correspo...Quantitative structure-activity relationships(QSARs)were determined using partial least square(PLS)and support vector machine(SVM).The predicted values by the final QSAR models were in good agreement with the corresponding experimental values.Chemical estrogenic activities are related to atomic properties(atomic Sanderson electronegativities,van der Waals volumes and polarizabilities).Comparison of the results obtained from two models,the SVM method exhibited better overall performances.Besides,three PLS models were constructed for some specific families based on their chemical structures.These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.展开更多
基金This work was supported by the National Natural Science Foundation of China （No.20673148）. We heartily thank the Molecular Discovery Ltd. for giving us the Dock 6.0 program as a freeware and the College of Life Sciences, Sun Yat-Sen University for the SYBYL 6.9 computation environment support.
文摘Three-dimensional quantitative structure activity relationship （3D-QSAR） and docking studies of a series of arylthioindole derivatives as tubulin inhibitors against human breast cancer cell line MCF-7 have been carried out. An optimal 3D-QSAR model from the comparative molecular field analysis （CoMFA） for training set with significant statistical quality （R2=0.898） and predictive ability （q2=0.654） was established. The same model was further applied to predict pIC50 values of the compounds in test set, and the resulting predictive correlation coefficient R2（pred） reaches 0.816, further showing that this CoMFA model has high predictive ability. Moreover, the appropriate binding orientations and conformations of these compounds interacting with tubulin are located by docking study, and it is very interesting to find the consistency between the CoMFA field distribution and the 3D topology structure of active site of tubulin. Based on CoMFA along with docking results, some important factors improving the activities of these compounds were discussed in detail and were summarized as follows： the substituents R3-R5 （on the phenyl ring） with higher electronegativity, the substituent R6 with higher eleetropositivity and bigger bulk, the substituent R7 with smaller bulk, and so on. In addition, five new compounds with higher activities have been designed. Such results can offer useful theoretical references for experimental works.
基金supported by Youth Foundation of the Education Department of Sichuan Province (No.09ZB038)
文摘In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using various types of chromosomes is used.The first method is a GA with binary chromosome（GA-BC） and the other is a GA with a fixed-length character chromosome（GA-FCC）.The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression（FSR） model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability（R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675） using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.
基金supported by the Foundation of Education Bureau,Sichuan Province (09ZB036)Technology Bureau,Sichuan Province (2006j13-141)
文摘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.
文摘A quantitative structure–activity relationship(QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using partial least squares regression coupled with stepwise forward–backward variable selection method. QSAR analyses were performed on the available IC50 D10 strains of Plasmodium falciparum data based on theoretical molecular descriptors. The QSAR model developed gave good predictive correlation coefficient(r2) of 0.8994, significant cross validated correlation coefficient(q2) of 0.7689, r2 for external test set)(2predr of 0.8256, coefficient of correlation of predicted data set)(2sepred,r of 0.3276. The model shows that antimalarial activity is greatly affected by donor and electron-withdrawing substituents. The study implicates that chalcone and dienone rings should have strong donor and electron-withdrawing substituents as they increase the activity of chalcone. Results show that the predictive ability of the model is satisfactory, and it can be used for designing similar group of antimalarial compounds. The findings derived from this analysis along with other molecular modeling studies will be helpful in designing of the new potent antimalarial activity of clinical utility.
基金the NSFC(Grant No.51878370)the National Special Program of Water Pollution Control and Management(2017ZX07202)the special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control(18L01ESPC).
文摘The abatements of 89 pharmaceuticals in secondary effluent by ozonation and the electro-peroxone(E-peroxone)process were investigated.Based on the results,a quantitative structure-activity relationship(QSAR)model was developed to explore relationship between chemical structure of pharmaceuticals and their oxidation rates by ozone.The orthogonal projection to latent structure(OPLS)method was used to identify relevant chemical descriptors of the pharmaceuticals,from large number of descriptors,for model development.The resulting QSAR model,based on 44 molecular descriptors related to the ozone reactivity of the pharmaceuticals,showed high goodness of fit(R^(2)=0.963)and predictive power(Q^(2)=0.84).After validation,the model was used to predict second-order rate constants of 491 pharmaceuticals of special concern(k_(O_(3)))including the 89 studied experimentally.The predicted k_(O_(3))values and experimentally determined pseudo-first order rate constants of the pharmaceuticals’abatement during ozonation(k_(OZ))and the E-peroxone process(k_(EP))were then used to assess effects of switching from ozonation to the E-peroxone process on removal of these pharmaceuticals.The results indicate that the E-peroxone process could accelerate the abatement of pharmaceuticals with relatively low ozone reactivity(k_(O_(3))<∼10^(2)M^(−1)⋅s^(−1))than ozonation(3–10 min versus 5–20 min).The validated QSAR model predicted 66 pharmaceuticals to be highly O_(3)-resistant.The developed QSAR model may be used to estimate the ozone reactivity of pharmaceuticals of diverse chemistry and thus predict their fate in ozone-based processes.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA11020405)the Key Research Program of the Chinese Academy of Sciences(Grant No.KZZD-EW-14).
文摘Quantitative structure-activity relationships(QSARs)were determined using partial least square(PLS)and support vector machine(SVM).The predicted values by the final QSAR models were in good agreement with the corresponding experimental values.Chemical estrogenic activities are related to atomic properties(atomic Sanderson electronegativities,van der Waals volumes and polarizabilities).Comparison of the results obtained from two models,the SVM method exhibited better overall performances.Besides,three PLS models were constructed for some specific families based on their chemical structures.These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.
