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A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

Xie H, Chen L, Zhang J, Xie X, Qiu K, Fu J - Int J Mol Sci (2015)

Bottom Line: The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes.The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885).This 3D QSAR approach provides significant insights that are useful for designing potent BRIs.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Yunnan University, Kunming 650091, China. front701228.student@sina.com.

ABSTRACT
B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

No MeSH data available.


Related in: MedlinePlus

Plots of observed vs. predicted activities of the training set and test set molecules from CoMSIA analysis.
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ijms-16-12307-f003: Plots of observed vs. predicted activities of the training set and test set molecules from CoMSIA analysis.

Mentions: We mainly focus on the CoMSIA obtained from pharmacophore-based alignment due to its satisfactory statistical results and its best predictive ability. As shown in Table 3, this CoMSIA model has a q2(r2cv) of 0.621 with ten optimal components, SEE of 0.063 and F value of 410.567, which indicates it is a quite good model. The corresponding field contributions of steric, electrostatic, hydrophobic, HBD and HBA are 0.196, 0.201, 0.291, 0.161 and 0.151, respectively, which suggests that each field gives similar contribution to activity. The observed and predicted pIC50 by the CoMSIA model of the training and test sets are given in Table 4, and the correlations between the observed and predicted pIC50 of training and test sets are depicted in Figure 3.


A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

Xie H, Chen L, Zhang J, Xie X, Qiu K, Fu J - Int J Mol Sci (2015)

Plots of observed vs. predicted activities of the training set and test set molecules from CoMSIA analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4490445&req=5

ijms-16-12307-f003: Plots of observed vs. predicted activities of the training set and test set molecules from CoMSIA analysis.
Mentions: We mainly focus on the CoMSIA obtained from pharmacophore-based alignment due to its satisfactory statistical results and its best predictive ability. As shown in Table 3, this CoMSIA model has a q2(r2cv) of 0.621 with ten optimal components, SEE of 0.063 and F value of 410.567, which indicates it is a quite good model. The corresponding field contributions of steric, electrostatic, hydrophobic, HBD and HBA are 0.196, 0.201, 0.291, 0.161 and 0.151, respectively, which suggests that each field gives similar contribution to activity. The observed and predicted pIC50 by the CoMSIA model of the training and test sets are given in Table 4, and the correlations between the observed and predicted pIC50 of training and test sets are depicted in Figure 3.

Bottom Line: The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes.The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885).This 3D QSAR approach provides significant insights that are useful for designing potent BRIs.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Yunnan University, Kunming 650091, China. front701228.student@sina.com.

ABSTRACT
B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

No MeSH data available.


Related in: MedlinePlus