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Binary and multi-class classification for androgen receptor agonists, antagonists and binders.

Geven Piir1, Sulev Sild1, Uko Maran1

  • 1University of Tartu, Institute of Chemistry, Ravila 14A, Tartu, 50411, Estonia.

Chemosphere
|November 13, 2020
PubMed
Summary
This summary is machine-generated.

Computational models predict chemical activity at the androgen receptor, aiding safety assessments. These QSAR models classify compounds as agonists, antagonists, or binders, supporting the evaluation of new chemicals before market release.

Keywords:
Androgen receptorBinary classificationEndocrine disrupting chemicalsMulti-class classificationQSARRandom forest

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Area of Science:

  • Endocrinology and Toxicology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Androgens and their receptor play crucial roles in human physiology.
  • Dysfunctional androgen receptor signaling is linked to various health issues, including cancer and infertility.
  • Assessing the androgenic activity of new chemicals is vital for public health and safety.

Purpose of the Study:

  • To develop predictive Quantitative Structure-Activity Relationship (QSAR) models for classifying compounds based on their androgen receptor activity.
  • To create models that can identify androgen agonists, antagonists, and binders.
  • To explore a multi-class approach for simultaneous discrimination of inactive compounds, agonists, and antagonists.

Main Methods:

  • Utilized a large dataset of chemicals from the CoMPARA project.
  • Developed random forest classification models for predicting androgen binding, agonistic, and antagonistic activities.
  • Implemented a multi-class classification strategy to differentiate between inactive, agonist, and antagonist compounds.

Main Results:

  • The classification models achieved 80% accuracy for predicting agonists, 72% for antagonists, and 78% for binders on the evaluation set.
  • A multi-class approach yielded a 64% prediction accuracy, indicating the complexity of simultaneous classification.
  • The developed models demonstrate good predictive performance, especially considering the dataset size and imbalance.

Conclusions:

  • Predictive QSAR models offer a viable computational alternative for assessing chemical activity at the androgen receptor.
  • These models can aid in the early screening of chemicals for potential endocrine-disrupting properties.
  • The developed classification models are publicly available in the QsarDB repository for further research and application.