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Prediction of selective estrogen receptor beta agonist using open data and machine learning approach
BACKGROUND: Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958355/ https://www.ncbi.nlm.nih.gov/pubmed/27486309 http://dx.doi.org/10.2147/DDDT.S110603 |
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author | Niu, Ai-qin Xie, Liang-jun Wang, Hui Zhu, Bing Wang, Sheng-qi |
author_facet | Niu, Ai-qin Xie, Liang-jun Wang, Hui Zhu, Bing Wang, Sheng-qi |
author_sort | Niu, Ai-qin |
collection | PubMed |
description | BACKGROUND: Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects. METHODS: Herein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods. RESULTS: The chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists. CONCLUSION: These results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists. |
format | Online Article Text |
id | pubmed-4958355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-49583552016-08-02 Prediction of selective estrogen receptor beta agonist using open data and machine learning approach Niu, Ai-qin Xie, Liang-jun Wang, Hui Zhu, Bing Wang, Sheng-qi Drug Des Devel Ther Original Research BACKGROUND: Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects. METHODS: Herein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods. RESULTS: The chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists. CONCLUSION: These results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists. Dove Medical Press 2016-07-18 /pmc/articles/PMC4958355/ /pubmed/27486309 http://dx.doi.org/10.2147/DDDT.S110603 Text en © 2016 Niu et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. |
spellingShingle | Original Research Niu, Ai-qin Xie, Liang-jun Wang, Hui Zhu, Bing Wang, Sheng-qi Prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
title | Prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
title_full | Prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
title_fullStr | Prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
title_full_unstemmed | Prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
title_short | Prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
title_sort | prediction of selective estrogen receptor beta agonist using open data and machine learning approach |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958355/ https://www.ncbi.nlm.nih.gov/pubmed/27486309 http://dx.doi.org/10.2147/DDDT.S110603 |
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