Structure-Based Quantitative Structure–Activity Relationship Modeling of Estrogen Receptor β-Ligands

Dong, Xueyi; Hilliard, Solomon G; Zheng, Weifan

doi:10.4155/fmc.11.49

Cited by 6 publications

(2 citation statements)

References 59 publications

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“…A variety of computational methods, including different docking programs and QSAR models, have proven to be successful in the identification process of new ER antagonists. − The first crystal structure determination of the ER ligand-binding domain (LBD) provided insight into the mode of action of ER ligands at the molecular level (Figure ). Currently, more than 60 crystal structures of both ERα and ERβ bound to a variety of agonists and antagonists are available in the protein data bank (PDB) .…”

Section: Resultsmentioning

confidence: 99%

Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery

Therrien

Englebienne

Arrowsmith

et al. 2011

J. Chem. Inf. Model.

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As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

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Section: Resultsmentioning

confidence: 99%

Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery

Therrien

Englebienne

Arrowsmith

et al. 2011

J. Chem. Inf. Model.

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“…Other structure-based pharmacophore identification programs can also be used. For example, in another report using the Molecular Operating Environment, a spheres were used to characterize the receptor's pharmacophore centers [21].…”

Section: Rd-qsar Methods Based On Pharmacophore and Pharmacophore Key Dmentioning

confidence: 99%

Receptor-dependent QSAR methods

Dong

Zheng

2013

In Silico Drug Discovery and Design

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She has over 10 years of experience in computational science, ranging from material chemistry to biomedical physics to drug design. She has authored more than 30 peer-reviewed scientific publications. Following her PhD work in growth and nucleation mechanism of diamond film under chemical vapor deposition environment, she became the Assistant Professor of Chemistry at Jilin University. Her expertise is in computational drug discovery and medical informatics. She is currently a Senior Research Scientist at the Biomanufacturing Research Institute and Technology Enterprise (NC, USA).Quantitative structure-activity relationship (QSAR) techniques can be classified into receptor-independent and receptor-dependent (RD-QSAR) groups. In this chapter, we first review a few RD-QSAR methods, and then focus on two distinct RD-QSAR techniques based on the intuitive concept of structure-based pharmacophore and pharmacophore key descriptors. The alignment-dependent method employs docking tools to generate the ligandbinding poses, which form the basis for a multiconformation/ multipose QSAR method. On the other hand, the alignment-free RD-QSAR technique uses pharmacophore key descriptors generated by comparing ligand pharmacophore patterns with those of the binding pocket. Both techniques have been successfully applied to analyze several QSAR datasets. We conclude that RD-QSAR techniques will play more important roles in QSAR modeling when increasing numbers of 3D structures of important biological targets become available.

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Four-and-a-half-LIM protein 1 down-regulates estrogen receptor α activity through repression of AKT phosphorylation in human breast cancer cell

Zhang

Feng

Yang

et al. 2012

The International Journal of Biochemistry & Cell Biology

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Structure-Based Quantitative Structure–Activity Relationship Modeling of Estrogen Receptor β-Ligands

Abstract: We have obtained an ensemble of predictive models of ERβ ligands that will be useful in the future discovery of novel ERβ-selective molecules.

Cited by 6 publications

References 59 publications

Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery

Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery

Receptor-dependent QSAR methods

Four-and-a-half-LIM protein 1 down-regulates estrogen receptor α activity through repression of AKT phosphorylation in human breast cancer cell

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