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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

A hybrid VS protocol based on pharmacophore hypothesis, genetic function approximation model, and molecular docking was applied to identify novel S6K1 inhibitors and 215 compounds with new scaffolds were selected
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Related In: Results  -  Collection


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Fig4: A hybrid VS protocol based on pharmacophore hypothesis, genetic function approximation model, and molecular docking was applied to identify novel S6K1 inhibitors and 215 compounds with new scaffolds were selected

Mentions: The three VS models of S6K1 inhibitors have been successfully constructed. Finally, the three methods have been combined in a hybrid protocol to virtual screen S6K1 inhibitors from the Specs database (202, 408 compounds) (Fig. 4). As shown in Fig. 4, the faster screening method, PB-VS, was used first. Building the 3D pharmacophore model is difficult because these reported S6K1 inhibitors are limited in structural diversity. In order to discover S6K1 inhibitors faster and more accurately, the GFA regression model that deduces the correlation between the selected five descriptors and the biological of present inhibitors was applied to re-filter the PB-VS screened compounds.Fig. 4


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)

A hybrid VS protocol based on pharmacophore hypothesis, genetic function approximation model, and molecular docking was applied to identify novel S6K1 inhibitors and 215 compounds with new scaffolds were selected
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: A hybrid VS protocol based on pharmacophore hypothesis, genetic function approximation model, and molecular docking was applied to identify novel S6K1 inhibitors and 215 compounds with new scaffolds were selected
Mentions: The three VS models of S6K1 inhibitors have been successfully constructed. Finally, the three methods have been combined in a hybrid protocol to virtual screen S6K1 inhibitors from the Specs database (202, 408 compounds) (Fig. 4). As shown in Fig. 4, the faster screening method, PB-VS, was used first. Building the 3D pharmacophore model is difficult because these reported S6K1 inhibitors are limited in structural diversity. In order to discover S6K1 inhibitors faster and more accurately, the GFA regression model that deduces the correlation between the selected five descriptors and the biological of present inhibitors was applied to re-filter the PB-VS screened compounds.Fig. 4

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