Limits...
Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Adekoya A, Dong X, Ebalunode J, Zheng W - Curr Chem Genomics (2009)

Bottom Line: The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed.As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors.Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

View Article: PubMed Central - PubMed

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

ABSTRACT
Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

No MeSH data available.


Related in: MedlinePlus

Fast convergence of the iterative PLS analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2802764&req=5

Figure 4: Fast convergence of the iterative PLS analysis.

Mentions: As shown in Fig. (4), the iPLS procedure to obtain the multi-conformational QSAR model converges very fast. In just 7 cycles of PLS regression, the models reach r2 of about 0.83, and maintain at that level. This procedure is highly efficient compared to other approaches, where all combinations of conformers need to be considered to build QSAR models, and stochastic optimization process is often used to select the best performing models. Because many conformers are allowed to represent an inhibitor molecule, conformational flexibility is taken into account in our QSAR models. This is better than traditional single conformer based 3D QSAR techniques, which is demonstrated in the following sections.


Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Adekoya A, Dong X, Ebalunode J, Zheng W - Curr Chem Genomics (2009)

Fast convergence of the iterative PLS analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2802764&req=5

Figure 4: Fast convergence of the iterative PLS analysis.
Mentions: As shown in Fig. (4), the iPLS procedure to obtain the multi-conformational QSAR model converges very fast. In just 7 cycles of PLS regression, the models reach r2 of about 0.83, and maintain at that level. This procedure is highly efficient compared to other approaches, where all combinations of conformers need to be considered to build QSAR models, and stochastic optimization process is often used to select the best performing models. Because many conformers are allowed to represent an inhibitor molecule, conformational flexibility is taken into account in our QSAR models. This is better than traditional single conformer based 3D QSAR techniques, which is demonstrated in the following sections.

Bottom Line: The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed.As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors.Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

View Article: PubMed Central - PubMed

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

ABSTRACT
Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

No MeSH data available.


Related in: MedlinePlus