We investigate a series of synthesized ${\beta}$-methoxyacrylate analogues for their 3D QSAR & HQSAR against Magnaporthe grisea (Rice Blast Disease). We perform the three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies, using the comparative molecular field anal...
We investigate a series of synthesized ${\beta}$-methoxyacrylate analogues for their 3D QSAR & HQSAR against Magnaporthe grisea (Rice Blast Disease). We perform the three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies, using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) procedure. In addition, we carry out a two-dimensional Quantitative Structure-Activity Relationship (2D-QSAR) study, using the Hologram QSAR (HQSAR). We perform these studies, using 53 compounds as a training set and 10 compounds as a test set. The predictive QSAR models have conventional $r^2$ values of 0.955 at CoMFA, 0.917 at CoMSIA, and 0.910 at HQSAR respectively; similarly, we obtain cross-validated coefficient $q^2$ values of 0.822 at CoMFA, 0.763 at CoMSIA, and 0.816 at HQSAR, respectively. From these studies, the CoMFA model performs better than the CoMSIA model.
We investigate a series of synthesized ${\beta}$-methoxyacrylate analogues for their 3D QSAR & HQSAR against Magnaporthe grisea (Rice Blast Disease). We perform the three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies, using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) procedure. In addition, we carry out a two-dimensional Quantitative Structure-Activity Relationship (2D-QSAR) study, using the Hologram QSAR (HQSAR). We perform these studies, using 53 compounds as a training set and 10 compounds as a test set. The predictive QSAR models have conventional $r^2$ values of 0.955 at CoMFA, 0.917 at CoMSIA, and 0.910 at HQSAR respectively; similarly, we obtain cross-validated coefficient $q^2$ values of 0.822 at CoMFA, 0.763 at CoMSIA, and 0.816 at HQSAR, respectively. From these studies, the CoMFA model performs better than the CoMSIA model.
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가설 설정
251, respectively. The CoMSIA hydrophobic and steric fields for the analysis are presented as contour maps in Figure 4. In general, the color polyhedral surrounded lattice points where the QSAR strongly associated changes in compound field values with changes in biological potency. A blue polyhedral surrounded regions where more hydrophobicity is favorable for increasing potency, whereas a red polyhedral surrounded regions where less hydrophobicity is good.
제안 방법
0 kcal/mol was used. Finally, non-cross-validated analysis was performed using the optimal number of previously identified components and was employed to analyze the results of CoMFA and CoMSIA.
To determine the similarity, the mutual distance between probe atom and the atoms of the molecules in the data set was considered. In this study, physicochemical properties, such as steric and electrostatic feature, hydrogen bond donor and acceptors, and hydrophobic field were considered. Eq.
Cross-validation was performed using the leave-one-out (LOO) method in which one compound is removed from the data set and its activity is predicted using the model derived from the rest of the data set. LOO cross validation was carried out with the number of components set equal to 10 and equal weights were assigned to steric and electrostatic fields, using CoMFA-STD, IND and H-bond scaling options. To speed up the analysis and reduce the noise, a minimum filter value 'σ' of 2.
A number of parameters were adjusted to optimize the HQSAR model by various fragment type, length and hologram length. The best model was built using atoms, bonds, and connectivity as fragment type 307 as hologram length and 5-8 as fragment size (Table 7). Activity prediction results by the HQSAR calculation are also summarized in Table 4 and Table 5.
대상 데이터
Partial least square (PLS) analysis. After eliminating 7 outlier compounds, 53 compounds were used for a training set and 10 compounds were utilized as a test set. Partial least squares (PLS) regression analysis was used in conjugation with the cross-validation option to determine the optimum number of components that, were then used in deriving the final 3D-QSAR model without cross-validation.
An HQSAR study on methoxyacrylate analogues indicated that this technique is able to efficiently correlate molecular structures with biological activity. An HQSAR module of SYBYL was used for the HQSAR study. The quality of the HQSAR model was assessed by statistical methods.
The predictive power of the model was also determined by using a test set. The 53 compounds were used for a training set and 10 compounds were used as a test set. A number of parameters were adjusted to optimize the HQSAR model by various fragment type, length and hologram length.
CoMSIA analysis. The CoMSIA of the QSAR module of SYBYL was used for the analysis. Similarity indices between a compound and a probe atom were calculated.
이론/모형
).14All Structures of the β-methoxyacrylate analogues were obtained through energy minimization with the Tripos force field, and partial atomic charges were added using the Gasteiger-Huckel method,15 with a 0.005 kcal/mol energy gradient conver gence criterion. Lowest energy conformation was searched by geometry optimization simulated annealing method and minimized conformation energy value was -12.
and lowest standard error of predictions (SEP) were taken as the optimum. Cross-validation was performed using the leave-one-out (LOO) method in which one compound is removed from the data set and its activity is predicted using the model derived from the rest of the data set. LOO cross validation was carried out with the number of components set equal to 10 and equal weights were assigned to steric and electrostatic fields, using CoMFA-STD, IND and H-bond scaling options.
After eliminating 7 outlier compounds, 53 compounds were used for a training set and 10 compounds were utilized as a test set. Partial least squares (PLS) regression analysis was used in conjugation with the cross-validation option to determine the optimum number of components that, were then used in deriving the final 3D-QSAR model without cross-validation. The cross validated coefficient, q2, were calculated using Eq.
성능/효과
The results of the CoMSIA analysis are summarized in Table 8, and actual and predicted activities of training and test set are shown in Table 4 and Table 5. The statistical results of the CoMSIA model in a variety of conditions between fields and grid spacing are shown in Table 3. CoMSIA used the observed pl50 values of β-methoxyacrylate derivatives as descriptors. Good cross-validated q2 (0.
참고문헌 (22)
Konradt, M.; Kappes, E. M.; Hiemer, M.; Peterson, H. H. GesundePflanzen 1996, 48(4), 126.
Dave, W. B.; John, M. C.; Jeremy, R. G. Pest Manag Sci. 2002, 58,649.
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