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High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures

Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology...

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Autores principales: Asako, Yuki, Uesawa, Yoshihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154693/
https://www.ncbi.nlm.nih.gov/pubmed/28441746
http://dx.doi.org/10.3390/molecules22040675
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author Asako, Yuki
Uesawa, Yoshihiro
author_facet Asako, Yuki
Uesawa, Yoshihiro
author_sort Asako, Yuki
collection PubMed
description Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. Owing to the correlation between our model’s and the challenge’s results, we consider that our model currently possesses the highest predictive power on agonist activity of the estrogen receptor ligand-binding domain. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.
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spelling pubmed-61546932018-11-13 High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures Asako, Yuki Uesawa, Yoshihiro Molecules Article Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. Owing to the correlation between our model’s and the challenge’s results, we consider that our model currently possesses the highest predictive power on agonist activity of the estrogen receptor ligand-binding domain. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules. MDPI 2017-04-23 /pmc/articles/PMC6154693/ /pubmed/28441746 http://dx.doi.org/10.3390/molecules22040675 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Asako, Yuki
Uesawa, Yoshihiro
High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
title High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
title_full High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
title_fullStr High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
title_full_unstemmed High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
title_short High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
title_sort high-performance prediction of human estrogen receptor agonists based on chemical structures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154693/
https://www.ncbi.nlm.nih.gov/pubmed/28441746
http://dx.doi.org/10.3390/molecules22040675
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