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A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.

Dong X, Zheng W - Curr Chem Genomics (2008)

Bottom Line: Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules.Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central, University, 1801 Fayetteville Street, Durham, NC 27707, USA.

ABSTRACT
We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

No MeSH data available.


Quality of the QSAR models depends on the number of principle components (PC) employed.
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Figure 7: Quality of the QSAR models depends on the number of principle components (PC) employed.

Mentions: To obtain predictive QSAR models, we systematically performed PLS analysis with different number of principle components used in the regression model. Fig. (7) shows how the number of principle components affects the quality of the resulting models for the best partitioning of training and test sets. The conventional correlation coefficient r2 obtained for the training set monotonically increases while the predictive R2 obtained for the testing set reaches a plateau before it starts to decrease. When the number of principle components (PC) is less than 3, the resulting QSAR models are not predictive for either the training set or the test set. When the number of principle components is over 8, the predictive ability for the test set starts to go down. This indicates potential over-training at higher number of principle components. According to our model validation protocol (Fig. 4), the models with the number of principle components between 5 and 8 are predictive for both the training and the test sets. The best models should be a trade-off between conventional r2 and the predictive R2. Also, to compare with other literature work which uses number of principle components of 6 [15], we decided to use 6 components to build the final best models.


A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.

Dong X, Zheng W - Curr Chem Genomics (2008)

Quality of the QSAR models depends on the number of principle components (PC) employed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2803435&req=5

Figure 7: Quality of the QSAR models depends on the number of principle components (PC) employed.
Mentions: To obtain predictive QSAR models, we systematically performed PLS analysis with different number of principle components used in the regression model. Fig. (7) shows how the number of principle components affects the quality of the resulting models for the best partitioning of training and test sets. The conventional correlation coefficient r2 obtained for the training set monotonically increases while the predictive R2 obtained for the testing set reaches a plateau before it starts to decrease. When the number of principle components (PC) is less than 3, the resulting QSAR models are not predictive for either the training set or the test set. When the number of principle components is over 8, the predictive ability for the test set starts to go down. This indicates potential over-training at higher number of principle components. According to our model validation protocol (Fig. 4), the models with the number of principle components between 5 and 8 are predictive for both the training and the test sets. The best models should be a trade-off between conventional r2 and the predictive R2. Also, to compare with other literature work which uses number of principle components of 6 [15], we decided to use 6 components to build the final best models.

Bottom Line: Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules.Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central, University, 1801 Fayetteville Street, Durham, NC 27707, USA.

ABSTRACT
We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

No MeSH data available.