Pharmacophore and QSAR modeling of estrogen receptor β ligands and subsequent validation and in silico search for new hits

Taha, Mutasem O.; Tarairah, Mai; Zalloum, Hiba; Abu-Sheikha, Ghassan

doi:10.1016/j.jmgm.2009.09.005

Cited by 35 publications

(27 citation statements)

References 89 publications

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“…These were imported into ChEMBL, and processed on the basis that the pKi values of active compounds (n = 436) were > 1.5, the pKi values of inactive compounds (n = 450) were < 1.0, the molecular weight was between 400 and 500, the ALogP was between -2 and 6, the number of hydrogen bond donors is < 4, and the number of hydrogen bond acceptors is < 8. The ROC curve analysis describes the sensitivity (true positive rate, Se) for any possible change in the number of selected compounds as 1 -Sp (specificity, which is defined as the true negative rate) [24].…”

Section: Materials and Methods Data Collection And Molecular Dockingmentioning

confidence: 99%

Discovery of CNS-Like D3R- Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening

Lee

Cho²,

Kim

2018

Preprint

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The dopamine D3 receptor is an important CNS target for the treatment of a variety of neurological diseases. Selective dopamine D3 receptor antagonists modulate the improvement of psychostimulant addiction and relapse. In this study, five and six featured pharmacophore models of D3R antagonists were generated and evaluated with the post-hoc score combining two survival scores of active and inactive. Among Top 10 models, APRRR215 and AHPRRR104 were chosen based on the coefficient of determination (APRRR215: R 2 training = 0.80; AHPRRR104: R 2 training = 0.82) and predictability (APRRR215: Q 2 test = 0.73, R 2 predictive = 0.82; AHPRRR104: Q 2 test = 0.86, R 2 predictive = 0.74) of their 3D-quantitative structure-activity relationship models. Pharmacophore-based virtual screening of a large compound library from eMolecules (> 3 million compounds) using two optimal models expedited the search process 100-fold speed increase compared to the docking-based screening (HTVS scoring function in Glide) and identified a series of hit compounds having promising novel scaffolds. After the screening, docking scores, as an adjuvant predictor, were added to two fitness scores (from the pharmacophore models) and predicted Ki (from PLSs of the QSAR models) to improve accuracy. Final selection of the most promising hit compounds were also evaluated for CNSlike properties as well as expected D3R antagonism.

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Section: Materials and Methods Data Collection And Molecular Dockingmentioning

confidence: 99%

Discovery of CNS-Like D3R- Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening

Lee

Cho²,

Kim

2018

Preprint

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“…CATALYST pharmacophores have been used as 3D queries for database searching and in 3D-QSAR studies. [54][55][56][57][58][59][60][61][62][63][64][65][66] Although pharmacophore modeling employing HYPOGEN has been heavily reviewed in the literature, [68][69][70][71][72][73][74][75][76] a brief discussion of this algorithm is provided herein to allow better readability of the chapter.…”

Section: The Algorithmmentioning

confidence: 99%

Mixing Pharmacophore Modeling and Classical QSAR Analysis as Powerful Tool for Lead Discovery

Taha¹

2012

Virtual Screening

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“…Both ERs have modest overall sequence identity, differing primarily in their N-terminus domains, with the sequences more conserved at the DNA (95 % identity) and ligand-binding domains (LBD) (58 % identity) (Angelis et al, 2005). Only two amino acids are different in binding pockets of ligand complexes with ERa and ERb: Leu384 in ERa is replaced by Met336 in ERb, and Met421 in ERa is replaced by Ile373 in ERb Angelis et al, 2005;Taha et al, 2010). These two ER subtypes have different tissue distribution patterns and perform different biological functions (Kong et al, 2003;Denger et al, 2001;Vu et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…In the past several years, the medicinal chemistry modeling studies of ERb ligands have been performed by different authors, including docking and quantitative structure-activity relationship (QSAR) techniques (Barrett et al, 2008;Wolohan and Reichert, 2007;Beeley and Sage, 2003;Norman et al, 2006;Henke et al, 2002;Taha et al, 2010;Asikainen et al, 2006;Li et al, 2006;Zhang et al, 2011;Waller, 2004;Xiao et al, 2008). Although X-ray crystal structures of ERb are available, docking approaches are not accurate to predict binding affinities of ERb ligands because of some serious problems (Barrett et al, 2008;Wolohan and Reichert, 2007;Beeley and Sage, 2003; Luan et al (2008) reported that accurate prediction of binding affinities of aryl diphenolic azoles remains difficult and the classification of binding affinities of diverse compounds to some extent may be feasible.…”

Section: Introductionmentioning

confidence: 99%

3D-QSAR and pharmacophore model study on aryl diphenolic azoles as estrogen receptor-β ligands

Zou

Huang

et al. 2013

Med Chem Res

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Pharmacophore and three-dimensional quantitative structure-activity relationship (3D-QSAR) model of 106 aryl diphenolic azoles as estrogen receptor-b ligands were proposed. The best pharmacophore model, five-site model including two hydrogen bond donors and three aromatic rings, has been established. The biological activity values (pIC 50 ) and classification (active or inactive) of compounds were predicted by 3D-QSAR model. The model correlation coefficient (R 2 ) is 0.9623. The classification accuracies in predicting for training, test, and total datasets are 97.5, 84.6, and 94.3 %, respectively. The 3D-QSAR model maps revealed that hydrogen bond donor, hydrophobic, and electron-withdrawing fields would be the essential factors for designing new ligands with higher biological activity. The results indicate that the combination of pharmacophore and 3D-QSAR analyses could be a powerful modeling tool for finding novel estrogen receptor-b ligands.

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Pharmacophore and QSAR modeling of estrogen receptor β ligands and subsequent validation and in silico search for new hits

Cited by 35 publications

References 89 publications

Discovery of CNS-Like D3R- Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening

Discovery of CNS-Like D3R- Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening

Mixing Pharmacophore Modeling and Classical QSAR Analysis as Powerful Tool for Lead Discovery

3D-QSAR and pharmacophore model study on aryl diphenolic azoles as estrogen receptor-β ligands

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