Limits...
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.


a. QSAR model-predicted activity versus the actual activity for the molecules in the training set. b. QSAR model-predicted activity versus the actual activity for the molecules in the test set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2803435&req=5

Figure 8: a. QSAR model-predicted activity versus the actual activity for the molecules in the training set. b. QSAR model-predicted activity versus the actual activity for the molecules in the test set.

Mentions: Fig. (8a) and Fig. (8b) show the predictive quality of the best QSAR models for PDE-4 inhibitors built with the SB-PPK descriptors and PLS method. Table 3 present the comparison between our best models with those of other methods. We have compared the results obtained with CoMFA [15], CoMSIA [15], 2D QSAR (using MOE package) and SB-PPK (our own method). All methods use PLS as the modeling tool with number of principle components set at 6. Both CoMFA and CoMSIA afforded highly predictive models based on the training set; however, the predictive R2 for the test set are both significantly lower than that for the training set. Our method, on the other hand, afforded much more balanced predictive quality, for both the training set and the test set. Thus, based on the reported correlation coefficients (for both training and test sets), we believe our method (SB-PPK plus the specific workflow process) afforded the best model both in terms of predictive ability for the training set and, more importantly, in terms of the predictive power on the test set.


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

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

a. QSAR model-predicted activity versus the actual activity for the molecules in the training set. b. QSAR model-predicted activity versus the actual activity for the molecules in the test set.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: a. QSAR model-predicted activity versus the actual activity for the molecules in the training set. b. QSAR model-predicted activity versus the actual activity for the molecules in the test set.
Mentions: Fig. (8a) and Fig. (8b) show the predictive quality of the best QSAR models for PDE-4 inhibitors built with the SB-PPK descriptors and PLS method. Table 3 present the comparison between our best models with those of other methods. We have compared the results obtained with CoMFA [15], CoMSIA [15], 2D QSAR (using MOE package) and SB-PPK (our own method). All methods use PLS as the modeling tool with number of principle components set at 6. Both CoMFA and CoMSIA afforded highly predictive models based on the training set; however, the predictive R2 for the test set are both significantly lower than that for the training set. Our method, on the other hand, afforded much more balanced predictive quality, for both the training set and the test set. Thus, based on the reported correlation coefficients (for both training and test sets), we believe our method (SB-PPK plus the specific workflow process) afforded the best model both in terms of predictive ability for the training set and, more importantly, in terms of the predictive power on the test set.

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.