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Garcia-Sosa, A. T.; Maran, U. Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. Int. J. Mol. Sci. 2021, 22, 6695.

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Garcia-Sosa, A. T.; Maran, U. Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. Int. J. Mol. Sci. 2021, 22, 6695.

QDB archive DOI: 10.15152/QDB.235   DOWNLOAD

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Property BindingClass: Activity in Androgen Receptor

Procedure_13: Multivariate Logistic Regression Model

Logistic regression (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 1685 0.916
Evaluation set validation 3882 0.884

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  • Garcia-Sosa, A. T.; Maran, U. Data for: Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. QsarDB repository, QDB.235. 2021. http://dx.doi.org/10.15152/QDB.235

  • Garcia-Sosa, A. T.; Maran, U. Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. Int. J. Mol. Sci. 2021, 22, 6695. http://dx.doi.org/10.3390/ijms22136695

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dc.date.accessioned 2021-06-22T14:49:58Z
dc.date.available 2021-06-22T14:49:58Z
dc.date.issued 2021-06-22
dc.identifier.uri http://hdl.handle.net/10967/235
dc.identifier.uri http://dx.doi.org/10.15152/QDB.235
dc.description.abstract Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen binding activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand- and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and Naive Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy.
dc.publisher Alfonso T. Garcia-Sosa
dc.publisher Uko Maran
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.title Garcia-Sosa, A. T.; Maran, U. Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. Int. J. Mol. Sci. 2021, 22, 6695.
qdb.property.endpoint 4. Human health effects 4.18. Endocrine Activity en_US
qdb.descriptor.application Glide en_US
qdb.descriptor.application JChem en_US
bibtex.entry article en_US
bibtex.entry.author Garcia-Sosa, A. T.
bibtex.entry.author Maran, U.
bibtex.entry.doi 10.3390/ijms22136695
bibtex.entry.journal Int. J. Mol. Sci. en_US
bibtex.entry.month Feb
bibtex.entry.pages 6695
bibtex.entry.title Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances en_US
bibtex.entry.volume 22
bibtex.entry.year 2021
qdb.model.type Logistic regression model (classification) en_US


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