Piir, G.; Sild, S.; Maran, U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere 2021, 262, 128313.

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Piir, G.; Sild, S.; Maran, U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere 2021, 262, 128313.

QDB archive DOI: 10.15152/QDB.236   DOWNLOAD

QsarDB content

Property Agonists: Activity in AR agonist pathway

Agonists_model: Agonist model

Random forest (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining681.000
OOBinternal validation680.824
Test setexternal validation15910.820
Evaluation setexternal validation46130.805

Property Antagonists: Activity in AR antagonist pathway

Antagonists_model: Antagonist model

Random forest (classification)

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NameTypenAccuracy
Training settraining2541.000
OOBinternal validation2540.858
Test setexternal validation12710.749
Evaluation setexternal validation38340.719

Property Binders: Activity in androgen receptor

Binders_model: Binding model

Random forest (classification)

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NameTypenAccuracy
Training settraining3161.000
OOBinternal validation3160.788
Test setexternal validation12160.760
Evaluation setexternal validation37170.778

Property MultiClass: Activity in AR agonist and antagonist pathway

MultiClass_model: Multi-Class model

Random forest (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining931.000
OOBinternal validation930.720
Test setexternal validation14350.674
Evaluation setexternal validation36830.638

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dc.date.accessioned2020-07-16T13:37:45Z
dc.date.available2020-07-16T13:37:45Z
dc.date.issued2020-07-16
dc.identifier.urihttp://hdl.handle.net/10967/236
dc.identifier.urihttp://dx.doi.org/10.15152/QDB.236
dc.description.abstractAndrogens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. Due to the large number of chemicals that require experimental testing, the computational methods are a viable alternative. Therefore, the aim of the present study was to develop predictive QSAR models for classifying compounds according to their activity at the androgen receptor. A large data set of chemicals from the CoMPARA project was used for this purpose and random forest classification models have been developed for androgen binding, agonist, and antagonist activity. In addition, a unique effort has been made for multi-class approach that discriminates between inactive compounds, agonists and antagonists simultaneously. For the evaluation set, the classification models predicted agonists with 80% of accuracy and for the antagonists’ and binders’ the respective metrics were 72% and 78%. Combining agonists, antagonists and inactive compounds into a multi-class approach added complexity to the modelling task and resulted to 64% prediction accuracy for the evaluation set. Considering the size of the training data sets and their imbalance, the achieved evaluation accuracy is very good.
dc.publisherGeven Piir
dc.publisherSulev Sild
dc.publisherUko Maran
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePiir, G.; Sild, S.; Maran, U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere 2021, 262, 128313.
qdb.property.endpoint4. Human health effects 4.18. Endocrine Activityen_US
qdb.descriptor.applicationDRAGON 6.0.38en_US
qdb.prediction.applicationR 3.5.3 / Random Forest 4.6-14en_US
bibtex.entryarticleen_US
bibtex.entry.authorPiir, G.
bibtex.entry.authorSild, S.
bibtex.entry.authorMaran, U.
bibtex.entry.doi10.1016/j.chemosphere.2020.128313
bibtex.entry.journalChemosphereen_US
bibtex.entry.pages128313
bibtex.entry.titleBinary and multi-class classification for androgen receptor agonists, antagonists and bindersen_US
bibtex.entry.volume262
bibtex.entry.year2021
qdb.model.typeRandom forest (classification)en_US
qdb.descriptor.calculationAgonists_model
qdb.descriptor.calculationAntagonists_model
qdb.descriptor.calculationBinders_model
qdb.descriptor.calculationMultiClass_model


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