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Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1.

Zhang H, Xiang ML, Liang JY, Zeng T, Zhang XN, Zhang J, Yang SY - Mol. Divers. (2013)

Bottom Line: Discovery of S6K1 inhibitors has thus attracted much attention in recent years.The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors.Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Northwest Normal University, Lanzhou , 730070, Gansu, People's Republic of China, zhanghui123gansu@163.com.

ABSTRACT
S6K1 has emerged as a potential target for the treatment for obesity, type II diabetes and cancer diseases. Discovery of S6K1 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening method that involves pharmacophore hypothesis, genetic function approximation (GFA) model, and molecular docking technology has been used to discover S6K1 inhibitors especially with novel scaffolds. The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors. Then, the molecular docking method was carried out to re-filter these screened compounds. Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

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Related in: MedlinePlus

Plot of the correlation between the experimental activity and the estimated activity by the best GFA model for the training set and test set compounds
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Related In: Results  -  Collection


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Fig3: Plot of the correlation between the experimental activity and the estimated activity by the best GFA model for the training set and test set compounds

Mentions: Fifty five compounds were used to train the GFA models and the remaining 18 compounds were used as a test set to evaluate the capacity of GFA models. Eight molecular property descriptors (ALogP, Molecular_Weight, Num_H_Donors, Num_H_Acceptors, Num_RotatableBonds, Num_Rings, Num_AromaticRings and Molecular_FractionalPolarSurfaceArea) and one structural fingerprint descriptor (ECFP_6) were employed in building the GFA models. Finally, ten GFA models were generated. The following criteria were used to evaluate the produced models capacity and suitability: (a) the lack of fit (LOF) score, (b) variable terms in the equation, and (c) the internal and external predictive ability of the equation. One GFA model showed greater correlation coefficient, lowest LOF and least possible intervariable correlation comparatively was selected to predict activity, in which five descriptors were finally selected to construct the GFA model equation (Molecular_Weight, Number_H_Donors, Alogp, Molecular_FractionalPolarSurfaceArea and ECFP_6). The correlation coefficients of the training set and test set are 0.97 and 0.76, respectively. Figure 3 shows the experimental VS estimated pIC50 of the training set and test set molecules for S6K1.Fig. 3


Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1.

Zhang H, Xiang ML, Liang JY, Zeng T, Zhang XN, Zhang J, Yang SY - Mol. Divers. (2013)

Plot of the correlation between the experimental activity and the estimated activity by the best GFA model for the training set and test set compounds
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3824193&req=5

Fig3: Plot of the correlation between the experimental activity and the estimated activity by the best GFA model for the training set and test set compounds
Mentions: Fifty five compounds were used to train the GFA models and the remaining 18 compounds were used as a test set to evaluate the capacity of GFA models. Eight molecular property descriptors (ALogP, Molecular_Weight, Num_H_Donors, Num_H_Acceptors, Num_RotatableBonds, Num_Rings, Num_AromaticRings and Molecular_FractionalPolarSurfaceArea) and one structural fingerprint descriptor (ECFP_6) were employed in building the GFA models. Finally, ten GFA models were generated. The following criteria were used to evaluate the produced models capacity and suitability: (a) the lack of fit (LOF) score, (b) variable terms in the equation, and (c) the internal and external predictive ability of the equation. One GFA model showed greater correlation coefficient, lowest LOF and least possible intervariable correlation comparatively was selected to predict activity, in which five descriptors were finally selected to construct the GFA model equation (Molecular_Weight, Number_H_Donors, Alogp, Molecular_FractionalPolarSurfaceArea and ECFP_6). The correlation coefficients of the training set and test set are 0.97 and 0.76, respectively. Figure 3 shows the experimental VS estimated pIC50 of the training set and test set molecules for S6K1.Fig. 3

Bottom Line: Discovery of S6K1 inhibitors has thus attracted much attention in recent years.The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors.Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Northwest Normal University, Lanzhou , 730070, Gansu, People's Republic of China, zhanghui123gansu@163.com.

ABSTRACT
S6K1 has emerged as a potential target for the treatment for obesity, type II diabetes and cancer diseases. Discovery of S6K1 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening method that involves pharmacophore hypothesis, genetic function approximation (GFA) model, and molecular docking technology has been used to discover S6K1 inhibitors especially with novel scaffolds. The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors. Then, the molecular docking method was carried out to re-filter these screened compounds. Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

Show MeSH
Related in: MedlinePlus