Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.
about
Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophoresPredictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public DataDocking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data.Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach.Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction.Effects of estrogens and estrogenic disrupting compounds on fish mineralized tissues.Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists.In silico identification and pharmacological evaluation of novel endocrine disrupting chemicals that act via the ligand-binding domain of the estrogen receptor α.Adverse outcome pathway (AOP) development I: strategies and principlesStructure-Based Understanding of Binding Affinity and Mode of Estrogen Receptor α Agonists and AntagonistsPrediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methodsTranscription factor activity of estrogen receptor α activation upon nonylphenol or bisphenol A treatment enhances the in vitro proliferation, invasion, and migration of neuroblastoma cells.Quantitative structure-activity relationship: promising advances in drug discovery platforms.Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening.Development of estrogen receptor beta binding prediction model using large sets of chemicals.Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches.Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.Conformal prediction to define applicability domain – A case study on predicting ER and AR binding
P2860
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P2860
Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.
description
2013 nî lūn-bûn
@nan
2013 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年学术文章
@wuu
2013年学术文章
@zh-cn
2013年学术文章
@zh-hans
2013年学术文章
@zh-my
2013年学术文章
@zh-sg
2013年學術文章
@yue
name
Identification of putative est ...... virtual screening approaches.
@ast
Identification of putative est ...... virtual screening approaches.
@en
Identification of putative est ...... virtual screening approaches.
@nl
type
label
Identification of putative est ...... virtual screening approaches.
@ast
Identification of putative est ...... virtual screening approaches.
@en
Identification of putative est ...... virtual screening approaches.
@nl
prefLabel
Identification of putative est ...... virtual screening approaches.
@ast
Identification of putative est ...... virtual screening approaches.
@en
Identification of putative est ...... virtual screening approaches.
@nl
P2093
P2860
P50
P1476
Identification of putative est ...... virtual screening approaches.
@en
P2093
Alexander Sedykh
Ashutosh Tripathi
Ivan Rusyn
Liying Zhang
Varnavas D Mouchlis
P2860
P356
10.1016/J.TAAP.2013.04.032
P577
2013-05-23T00:00:00Z