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3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors.

Xie H, Qiu K, Xie X - Int J Mol Sci (2014)

Bottom Line: In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models.The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601).This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs.

View Article: PubMed Central - PubMed

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

ABSTRACT
Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.

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Plots of observed versus predicted activities of the training set and test set molecules from CoMFA analysis.
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ijms-15-20927-f002: Plots of observed versus predicted activities of the training set and test set molecules from CoMFA analysis.

Mentions: In order to validate the obtained 3D QSAR models, r2pred was used to determine the predictive abilities of the CoMFA and CoMSIA models from the 16 compounds (test set), which were not included in the generation of the models. The obtained r2pred of the test set is 0.658, 0.601 for the CoMFA, CoMSIA model, respectively, which indicates that both models have good predictive ability. The observed and predicted pIC50 of the training and test sets by the CoMFA and CoMSIA models are listed in Table 3, and the correlations between the observed and predicted pIC50 of training and test sets are depicted in Figure 2 for CoMFA model, Figure 3 for CoMSIA model, respectively.


3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors.

Xie H, Qiu K, Xie X - Int J Mol Sci (2014)

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

License
Show All Figures
getmorefigures.php?uid=PMC4264204&req=5

ijms-15-20927-f002: Plots of observed versus predicted activities of the training set and test set molecules from CoMFA analysis.
Mentions: In order to validate the obtained 3D QSAR models, r2pred was used to determine the predictive abilities of the CoMFA and CoMSIA models from the 16 compounds (test set), which were not included in the generation of the models. The obtained r2pred of the test set is 0.658, 0.601 for the CoMFA, CoMSIA model, respectively, which indicates that both models have good predictive ability. The observed and predicted pIC50 of the training and test sets by the CoMFA and CoMSIA models are listed in Table 3, and the correlations between the observed and predicted pIC50 of training and test sets are depicted in Figure 2 for CoMFA model, Figure 3 for CoMSIA model, respectively.

Bottom Line: In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models.The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601).This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs.

View Article: PubMed Central - PubMed

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

ABSTRACT
Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.

Show MeSH
Related in: MedlinePlus