QSAR models for anti-androgenic effect – a preliminary study
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Summary
Three modelling systems were used for construction of androgenic receptor antagonist models and different descriptors in the modelling systems are illustrated with hydroxyflutamide and dexamethasone as examples (a non-steroid and a steroid anti-androgen, respectively).
- Type
- article
- Published
- 2011-01-01
- Cited by
- 22
- References
- 55
- OpenAlex
- https://openalex.org/W1981389926
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:22049514
Keywords
Quantitative structure–activity relationship, Chemistry, Pharmacology, Computational biology, Medicine
References
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Cited by
- Conformal prediction to define applicability domain – A case study on predicting ER and AR binding
- Mixtures of endocrine disrupting contaminants modelled on human high end exposures: an exploratory study in rats.
- Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors.
- QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project.
- Adverse effects on sexual development in rat offspring after low dose exposure to a mixture of endocrine disrupting pesticides.
- Prediction of the endocrine disruption profile of pesticides$
- Identifying potential endocrine disruptors among industrial chemicals and their metabolites--development and evaluation of in silico tools.
- QSAR Model for Androgen Receptor Antagonism - Data from CHO Cell Reporter Gene Assays
- Mixtures of endocrine-disrupting contaminants induce adverse developmental effects in preweaning rats.
- Are structural analogues to bisphenol a safe alternatives?
- QSAR study of aquatic toxicity by chemometrics methods in the framework of REACH regulation
- Developmental toxicity effects in experimental animals after mixed exposure to endocrine disrupting pesticides
- Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering
- An effect-directed strategy for characterizing emerging chemicals in food contact materials made from paper and board.
- Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals
- Computational prediction models for assessing endocrine disrupting potential of chemicals
- QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches
- Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances
- Deliverable D 2 . 2 – Report Report on the use of in ‐ silico methods for the prioritisation of substances and mixtures thereof
- General Guidance on Endocrine Assessment: Assays and Endpoints
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