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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.


a. The SB-PPK descriptor values of a PDE4 inhibitor (mol.3 of Table 1). b. The SB-PPK descriptor values averaged over the 35 PDE4 Inhibitors.
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Figure 6: a. The SB-PPK descriptor values of a PDE4 inhibitor (mol.3 of Table 1). b. The SB-PPK descriptor values averaged over the 35 PDE4 Inhibitors.

Mentions: The descriptors of the PDE-4 inhibitors were determined based on the matches between the feature pairs in the reference and those in each individual molecule. Fig. (6a) shows the values of calculated SB-PPK descriptors of PDE-4 inhibitor 3 (mol.3 in Table 1). The average descriptor values over the dataset of 35 PDE-4 inhibitors are presented in Fig. (6b). In inhibitor 3, the value of AL10 is 4, which indicates 4 possible AL10 (inter-feature distance 3.98) matches between the feature pairs of inhibitor 3 and those of the binding site reference. Obviously, different molecules have different number of matches between different feature pairs. In Fig. (6b), the error bars show the variations of each descriptor value around the averages, amongst the different inhibitors in the dataset under study. Those descriptors with larger variations tend to have more important roles in QSAR 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)

a. The SB-PPK descriptor values of a PDE4 inhibitor (mol.3 of Table 1). b. The SB-PPK descriptor values averaged over the 35 PDE4 Inhibitors.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: a. The SB-PPK descriptor values of a PDE4 inhibitor (mol.3 of Table 1). b. The SB-PPK descriptor values averaged over the 35 PDE4 Inhibitors.
Mentions: The descriptors of the PDE-4 inhibitors were determined based on the matches between the feature pairs in the reference and those in each individual molecule. Fig. (6a) shows the values of calculated SB-PPK descriptors of PDE-4 inhibitor 3 (mol.3 in Table 1). The average descriptor values over the dataset of 35 PDE-4 inhibitors are presented in Fig. (6b). In inhibitor 3, the value of AL10 is 4, which indicates 4 possible AL10 (inter-feature distance 3.98) matches between the feature pairs of inhibitor 3 and those of the binding site reference. Obviously, different molecules have different number of matches between different feature pairs. In Fig. (6b), the error bars show the variations of each descriptor value around the averages, amongst the different inhibitors in the dataset under study. Those descriptors with larger variations tend to have more important roles in QSAR 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.