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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.


The distribution of biological activity (-logM) of the 35 indole derivatives.
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Figure 1: The distribution of biological activity (-logM) of the 35 indole derivatives.

Mentions: This dataset covers a variety of molecules with various levels of inhibitory activities against PDE-4. Fig. (1) shows the activity distribution of the 35 indole derivatives. To develop and rigorously validate a QSAR model, 28 molecules were selected as the training set, and the remaining 7 molecules were held out as the test set. The specific selection of the training and test sets was determined by either randomly splitting or clustering-based splitting of the original dataset.


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

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

The distribution of biological activity (-logM) of the 35 indole derivatives.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The distribution of biological activity (-logM) of the 35 indole derivatives.
Mentions: This dataset covers a variety of molecules with various levels of inhibitory activities against PDE-4. Fig. (1) shows the activity distribution of the 35 indole derivatives. To develop and rigorously validate a QSAR model, 28 molecules were selected as the training set, and the remaining 7 molecules were held out as the test set. The specific selection of the training and test sets was determined by either randomly splitting or clustering-based splitting of the original dataset.

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.