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Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations.

Wang Y, Wu M, Ai C, Wang Y - Int J Mol Sci (2015)

Bottom Line: By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored.Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q(2) (cross-validated correlation coefficient) = 0.557, R(2)ncv (non-cross-validated correlation coefficient) = 0.740, R(2)pre (predicted correlation coefficient) = 0.749 and Q(2) = 0.598, R(2)ncv = 0.767, R(2)pre = 0.860, respectively).Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions.

View Article: PubMed Central - PubMed

Affiliation: Lab of Systems Pharmacology, College of Life Sciences, Northwest A&F (Agriculture and Forestry) University, Yangling 712100, China. yuan-w-09@nwsuaf.edu.cn.

ABSTRACT
Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q(2) (cross-validated correlation coefficient) = 0.557, R(2)ncv (non-cross-validated correlation coefficient) = 0.740, R(2)pre (predicted correlation coefficient) = 0.749 and Q(2) = 0.598, R(2)ncv = 0.767, R(2)pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.

No MeSH data available.


Related in: MedlinePlus

Self-organizing map showing the distribution of the training and test sets. The test set is labeled in red and the training set in black, respectively. The number equals to the series number of the molecules of the TNF-α release inhibitors.
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ijms-16-20118-f001: Self-organizing map showing the distribution of the training and test sets. The test set is labeled in red and the training set in black, respectively. The number equals to the series number of the molecules of the TNF-α release inhibitors.

Mentions: In the present work, a total of 1664 molecular descriptors were calculated for each TNF-α release inhibitor by Dragon (version 5.4). Then, based on these descriptors as input vectors, a SOM with 6 × 6 neurons was generated for the dataset. Figure 1 shows the SOM for TNF-α release inhibitors, in which the test set is labeled in red and the training set in black, respectively. It is clear that the entire distribution of the molecules in the map is satisfactory and both sets present a uniform spread in the whole chemical space. Furthermore, the representative points in the test set are close to those in the training set. The above results indicate that the division of the dataset is reliable and rational.


Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations.

Wang Y, Wu M, Ai C, Wang Y - Int J Mol Sci (2015)

Self-organizing map showing the distribution of the training and test sets. The test set is labeled in red and the training set in black, respectively. The number equals to the series number of the molecules of the TNF-α release inhibitors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4613192&req=5

ijms-16-20118-f001: Self-organizing map showing the distribution of the training and test sets. The test set is labeled in red and the training set in black, respectively. The number equals to the series number of the molecules of the TNF-α release inhibitors.
Mentions: In the present work, a total of 1664 molecular descriptors were calculated for each TNF-α release inhibitor by Dragon (version 5.4). Then, based on these descriptors as input vectors, a SOM with 6 × 6 neurons was generated for the dataset. Figure 1 shows the SOM for TNF-α release inhibitors, in which the test set is labeled in red and the training set in black, respectively. It is clear that the entire distribution of the molecules in the map is satisfactory and both sets present a uniform spread in the whole chemical space. Furthermore, the representative points in the test set are close to those in the training set. The above results indicate that the division of the dataset is reliable and rational.

Bottom Line: By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored.Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q(2) (cross-validated correlation coefficient) = 0.557, R(2)ncv (non-cross-validated correlation coefficient) = 0.740, R(2)pre (predicted correlation coefficient) = 0.749 and Q(2) = 0.598, R(2)ncv = 0.767, R(2)pre = 0.860, respectively).Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions.

View Article: PubMed Central - PubMed

Affiliation: Lab of Systems Pharmacology, College of Life Sciences, Northwest A&F (Agriculture and Forestry) University, Yangling 712100, China. yuan-w-09@nwsuaf.edu.cn.

ABSTRACT
Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q(2) (cross-validated correlation coefficient) = 0.557, R(2)ncv (non-cross-validated correlation coefficient) = 0.740, R(2)pre (predicted correlation coefficient) = 0.749 and Q(2) = 0.598, R(2)ncv = 0.767, R(2)pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.

No MeSH data available.


Related in: MedlinePlus