SAR/QSAR methods in public health practice

Demchuk, Eugene; Ruíz, Patricia; Chou, Selene; Fowler, Bruce A.

doi:10.1016/j.taap.2010.10.017

Cited by 28 publications

(15 citation statements)

References 21 publications

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“…Silico tools provide a means of assessing toxicity of chemicals that lack of appropriate experimental test data (Fielder et al, 1990;Cronin and Dearden, 1995;Greene, 2002;Tsakovska et al, 2008;Demchuk et al, 2011;Schultz and Seward, 2000;Simon-Hettich et al, 2006;Jaworska et al, 2003;Ruiz et al, 2011). Most of in silico prediction systems used to estimate qualitative and quantitative toxicity are either SAR or QSAR models (Gombar, 1997;Klopman et al, 2004;Richard and Benigni, 2002;Enslein, 1998;Cronin et al, 2003).…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Quantitative structure–toxicity relationship (QSTR) studies on the organophosphate insecticides

Can

2014

Toxicology Letters

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Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Quantitative structure–toxicity relationship (QSTR) studies on the organophosphate insecticides

Can

2014

Toxicology Letters

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“…Structure-activity modeling plays an important role in government programs in support of protecting human populations from exposure to environmental contaminants (Demchuk et al , 2011). Specifically, computational methods to identify chemicals that may pose endocrine disruption hazard for additional in vitro or in vivo testing are important prioritization approaches (Lo Piparo and Worth, 2010; Tsakovska et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Zhang

Sedykh

Tripathi

et al. 2013

Toxicology and Applied Pharmacology

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Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.

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“…QSAR has been applied successfully and extensively to ind predictive models for activity of bioactive agents for the toxicity prediction [26][27][28][29], activity of peptides [30][31][32][33], drug metabolism [34][35][36], gastrointestinal absorption [37][38][39], prediction of pharmacokinetic and ADME properties [40][41][42][43][44], drug resistance and physicochemical properties [45][46][47].…”

Section: Quantitative Structure Activity Relationshipmentioning

confidence: 99%

Introductory Chapter: Some Quantitative Structure Activity Relationship Descriptor

Kandemirli¹

2017

Quantitative Structure-Activity Relationship

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SAR/QSAR methods in public health practice

Cited by 28 publications

References 21 publications

Quantitative structure–toxicity relationship (QSTR) studies on the organophosphate insecticides

Quantitative structure–toxicity relationship (QSTR) studies on the organophosphate insecticides

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Introductory Chapter: Some Quantitative Structure Activity Relationship Descriptor

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