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Classification and Virtual Screening of Androgen Receptor Antagonists
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Handbook of Molecular Descriptors
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A Chemically Intuitive Molecular Index Based on the Eigenvalues of a Modified Adjacency Matrix
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The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
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Principles of QSAR models validation: internal and external
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Rapid and Sensitive Reporter Gene Assays for Detection of Antiandrogenic and Estrogenic Effects of Environmental Chemicals
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Novel Software Tools for Chemical Diversity
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The importance of molecular structures, endpoints’ values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders
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CP-MLR/PLS directed QSAR study on apical sodium-codependent bile acid transporter inhibition activity of benzothiepines
inferredcites →https://doi.org/10.1016/b978-0-12-571149-4.50017-9openalex-snapshot
The Androgen Receptor: An Overview
inferredcites →https://doi.org/10.1016/j.bmc.2006.06.064openalex-snapshot
Structural basis for androgen receptor agonists and antagonists: Interaction of SPEED 98-listed chemicals and related compounds with the androgen receptor based on an in vitro reporter gene assay and 3D-QSAR
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Exploring the impact of size of training sets for the development of predictive QSAR models
inferredcites →https://doi.org/10.1016/j.compbiomed.2009.06.012openalex-snapshot
Feature extraction and dimensionality reduction for mass spectrometry data
inferredcites →https://doi.org/10.1016/j.jmgm.2007.01.003openalex-snapshot
In silico screening of estrogen-like chemicals based on different nonlinear classification models
inferredcites →https://doi.org/10.1016/j.tiv.2004.10.008openalex-snapshot
Evaluation of a rapid in vitro androgen receptor transcriptional activation assay using AR-EcoScreen™ cells
inferredcites →https://doi.org/10.1016/j.tiv.2005.04.009openalex-snapshot
Screening for androgen receptor activities in 253 industrial chemicals by in vitro reporter gene assays using AR-EcoScreenTM cells
inferredcites →https://doi.org/10.1016/j.toxlet.2004.11.014openalex-snapshot
Examination of the in vitro (anti)estrogenic, (anti)androgenic and (anti)dioxin-like activities of tetralin, indane and isochroman derivatives using receptor-specific bioassays
inferredcites →https://doi.org/10.1016/s0169-7439(97)00030-0openalex-snapshot
Kohonen and counterpropagation artificial neural networks in analytical chemistry
inferredcites →https://doi.org/10.1021/ci00063a011openalex-snapshot
Molecular identification number for substructure searches
inferredcites →https://doi.org/10.1021/ci015504aopenalex-snapshot
Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors
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Development of Quantitative Structure−Activity Relationships and Classification Models for Anticonvulsant Activity of Hydantoin Analogues
inferredcites →https://doi.org/10.1021/ci049923uopenalex-snapshot
Validated QSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting into Training−Test Sets and Consensus Modeling
inferredcites →https://doi.org/10.1021/ci050296yopenalex-snapshot
The Use of Consensus Scoring in Ligand-Based Virtual Screening
inferredcites →https://doi.org/10.1021/ci050529lopenalex-snapshot
Introducing the Consensus Modeling Concept in Genetic Algorithms: Application to Interpretable Discriminant Analysis
inferredcites →https://doi.org/10.1021/ci700141xopenalex-snapshot
Chemical Data Mining of the NCI Human Tumor Cell Line Database
inferredcites →https://doi.org/10.1021/ci900080fopenalex-snapshot
A Model-Based Ensembling Approach for Developing QSARs
inferredcites →https://doi.org/10.1021/ci900203nopenalex-snapshot
Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity
inferredcites →https://doi.org/10.1021/ci980033mopenalex-snapshot
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
inferredcites →https://doi.org/10.1021/cr020456uopenalex-snapshot
Chemistry and Structural Biology of Androgen Receptor
inferredcites →https://doi.org/10.1021/tx030011gopenalex-snapshot
Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor
inferredcites →https://doi.org/10.1021/tx0601509openalex-snapshot
QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles
inferredcites →https://doi.org/10.1021/tx7002382openalex-snapshot
Screening of 397 Chemicals and Development of a Quantitative Structure−Activity Relationship Model for Androgen Receptor Antagonism
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot
Gene Selection for Cancer Classification using Support Vector Machines
inferredcites →https://doi.org/10.1023/a:1025386326946openalex-snapshot
Rational selection of training and test sets for the development of validated QSAR models
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1080/10629360802550606openalex-snapshot
Binary classification models for endocrine disrupter effects mediated through the estrogen receptor
inferredcites →https://doi.org/10.1145/1656274.1656278openalex-snapshot
The WEKA data mining software
inferredcites →https://doi.org/10.1146/annurev.bi.63.070194.002315openalex-snapshot
MOLECULAR MECHANISMS OF ACTION OF STEROID/THYROID RECEPTOR SUPERFAMILY MEMBERS
inferredcites →https://doi.org/10.1289/ehp.112-1241964openalex-snapshot
Interlaboratory Comparison of Four in Vitro Assays for Assessing Androgenic and Antiandrogenic Activity of Environmental Chemicals
inferredcites →https://doi.org/10.1289/ehp.5758openalex-snapshot
Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.
inferredcites →https://doi.org/10.1289/ehp.6649openalex-snapshot
Screening for estrogen and androgen receptor activities in 200 pesticides by in vitro reporter gene assays using Chinese hamster ovary cells.
inferredcites →https://doi.org/10.1289/ehp.95103s7135openalex-snapshot
Environmental estrogens: health implications for humans and wildlife.
inferredcites →https://doi.org/10.1897/01-198openalex-snapshot
Structure-activity relationship approaches and applications
inferredcites →https://doi.org/10.2174/138620709788489037openalex-snapshot
The Applications of Machine Learning Algorithms in the Modeling of Estrogen-Like Chemicals
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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On the Development and Validation of QSAR Models
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Molecular Modeling Method Applications: Probing the Mechanism of Endocrine Disruptor Action
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Prioritization of Chemicals Based on Chemoinformatic Analysis
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Prioritization of Chemicals Based on Chemoinformatic Analysis
inferred← citeshttps://doi.org/10.1016/b978-0-12-396461-8.00002-6openalex-snapshot
Quantitative Structure-Activity Relationship (QSAR) Models, Physiologically Based Pharmacokinetic (PBPK) Models, Biologically Based Dose Response (BBDR) and Toxicity Pathways
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Machine learning methods for endocrine disrupting potential identification based on single-cell data
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Identifying potential endocrine disruptors among industrial chemicals and their metabolites – development and evaluation of in silico tools
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Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2018.12.131openalex-snapshot
Development, validation and integration of in silico models to identify androgen active chemicals
inferred← citeshttps://doi.org/10.1016/j.cplett.2018.06.022openalex-snapshot
Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2013.03.008openalex-snapshot
In silico study on hydroxylated polychlorinated biphenyls as androgen receptor antagonists
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2014.08.026openalex-snapshot
Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2011.03.008openalex-snapshot
QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants
inferred← citeshttps://doi.org/10.1016/j.jmgm.2011.09.002openalex-snapshot
Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries
inferred← citeshttps://doi.org/10.1016/j.jsbmb.2013.04.009openalex-snapshot
In silico methods in the discovery of endocrine disrupting chemicals
inferred← citeshttps://doi.org/10.1016/j.jsbmb.2015.08.015openalex-snapshot
Reprint of “In silico methods in the discovery of endocrine disrupting chemicals”
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Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals
inferred← citeshttps://doi.org/10.1021/ci300514vopenalex-snapshot
Identification of Novel Androgen Receptor Antagonists Using Structure- and Ligand-Based Methods
inferred← citeshttps://doi.org/10.1021/ci4003766openalex-snapshot
Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation
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Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines
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Deriving general structure–activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods
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Computational QSAR model combined molecular descriptors and fingerprints to predict HDAC1 inhibitors
inferred← citeshttps://doi.org/10.1080/1062936x.2010.528254openalex-snapshot
QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs
inferred← citeshttps://doi.org/10.1080/1062936x.2012.657235openalex-snapshot
QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1104809openalex-snapshot
Prediction of the endocrine disruption profile of pesticides
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Prediction ofin vitroandin vivooestrogen receptor activity using hierarchical clustering
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Conformal prediction to define applicability domain – A case study on predicting ER and AR binding
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Identification and Characterization of MEL-3, a Novel AR Antagonist That Suppresses Prostate Cancer Cell Growth
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1008191openalex-snapshot
Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms
inferred← citeshttps://doi.org/10.14288/1.0073209openalex-snapshot
Quantitative structure-activity relationship based virtual screening for novel androgen receptor antagonists
inferred← citeshttps://doi.org/10.14288/1.0165754openalex-snapshot
Discovery of novel androgen receptor inhibitors as prospective therapeutics for advanced prostate cancer
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Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer
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Guidance for the identification of endocrine disruptors in the context of Regulations (EU) No 528/2012 and (EC) No 1107/2009
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Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns
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Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances
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Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products
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Principles of QSAR Modeling
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Clustering Genes, Tissues, Cells and Bioactive Chemicals by Sphere SOM
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