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Correction published on 5 March 2015, see Int. J. Mol. Sci. 2015, 16(3), 5072-5075.

Open AccessArticle
Int. J. Mol. Sci. 2014, 15(11), 20927-20947; doi:10.3390/ijms151120927

3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors

1
Department of Chemistry, Yunnan University, Kunming 650091, Yunnan, China
2
Department of Chemistry, School of Pharmaceutical Science & Yunnan Key Laboratory of Pharmacology for Natural Products, Kunming Medical University, Kunming 650500, Yunnan, China
*
Authors to whom correspondence should be addressed.
Received: 27 July 2014 / Revised: 28 September 2014 / Accepted: 22 October 2014 / Published: 14 November 2014
(This article belongs to the Special Issue Chemical Bond and Bonding 2015)
View Full-Text   |   Download PDF [527 KB, uploaded 14 November 2014]   |  

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, r2ncv = 0.988, r2pred = 0.658; CoMSIA: q2 = 0.843, r2ncv = 0.989, r2pred = 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. View Full-Text
Keywords: steroidal aromatase inhibitors; 3D QSAR; CoMFA; CoMSIA; pharmacophore; virtual screening steroidal aromatase inhibitors; 3D QSAR; CoMFA; CoMSIA; pharmacophore; virtual screening
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xie, H.; Qiu, K.; Xie, X. 3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors. Int. J. Mol. Sci. 2014, 15, 20927-20947.

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