Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts - Wikidata - awgldk - TriplyDB

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts

http://www.wikidata.org/entity/Q24815373

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Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity Estrogenic activity of styrene oligomers after metabolic activation by rat liver microsomes.Large effects from small exposures. I. Mechanisms for endocrine-disrupting chemicals with estrogenic activity Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A.Approaches to the assessment of toxicity data with endpoints related to endocrine disruption.Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction.Decision forest analysis of 61 single nucleotide polymorphisms in a case-control study of esophageal cancer; a novel method.The EDKB: an established knowledge base for endocrine disrupting chemicals.Free energies of ligand binding for structurally diverse compounds.Versatility or promiscuity: the estrogen receptors, control of ligand selectivity and an update on subtype selective ligands.Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists.The adrenal cortex and steroidogenesis as cellular and molecular targets for toxicity: critical omissions from regulatory endocrine disrupter screening strategies for human health?Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor.Human breast milk and xenoestrogen exposure: a possible impact on human health.SAR and QSAR modeling of endocrine disruptors.Quantitative structure-activity relationships predicting the antioxidant potency of 17β-estradiol-related polycyclic phenols to inhibit lipid peroxidation.Assessing and managing risks arising from exposure to endocrine-active chemicals.sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products Screening for potential endocrine disruptors in fish: evidence from structural alerts and in vitro and in vivo toxicological assays.QSAR Models at the US FDA/NCTR.Structure-activity approach to the identification of environmental estrogens: the MCASE approach.An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity.Parabens, oestrogenicity, underarm cosmetics and breast cancer: a perspective on a hypothesis.Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH).Molecular modeling and molecular dynamics simulation studies on the interactions of hydroxylated polychlorinated biphenyls with estrogen receptor-β.Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis.Prediction of binding affinity for estrogen receptor alpha modulators using statistical learning approaches.Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents.Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations.

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Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts

http://www.wikidata.org/entity/Q24815373

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2002 nî lūn-bûn

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2002 թուականի Յունուարին հրատարակուած գիտական յօդուած

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Prediction of estrogen recepto ...... d model with structural alerts

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Prediction of estrogen recepto ...... d model with structural alerts

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Prediction of estrogen recepto ...... d model with structural alerts

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Environmental Health Perspectives

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Prediction of estrogen recepto ...... d model with structural alerts

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Daniel M Sheehan

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Hong Fang

http://www.w3.org/2001/XMLSchema#string

Huixiao Hong

http://www.w3.org/2001/XMLSchema#string

Jie Wu

http://www.w3.org/2001/XMLSchema#string

John D Walker

http://www.w3.org/2001/XMLSchema#string

Leming Shi

http://www.w3.org/2001/XMLSchema#string

Qian Xie

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Roger Perkins

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William Branham

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P2860

Molecular basis of agonism and antagonism in the oestrogen receptor

The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen

QSAR models using a large diverse set of estrogens.

Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens.

The estradiol pharmacophore: ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site.

Research needs for the risk assessment of health and environmental effects of endocrine disruptors: a report of the U.S. EPA-sponsored workshop.

Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species.

Quantitative comparisons of in vitro assays for estrogenic activities.

Chemical selection by the Interagency Testing Committee: use of computerized substructure searching to identify chemical groups for health effects, chemical fate and ecological effects testing.

Discovery of HIV-1 integrase inhibitors by pharmacophore searching.

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29-36

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3272743

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10.1289/EHP.0211029

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1

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110

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11577340

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11781162

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1240690

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