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      Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

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          Abstract

          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.

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          Most cited references 26

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          Random forest

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            Random forest: a classification and regression tool for compound classification and QSAR modeling.

            A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
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              Estrogen receptors: how do they signal and what are their targets.

              During the past decade there has been a substantial advance in our understanding of estrogen signaling both from a clinical as well as a preclinical perspective. Estrogen signaling is a balance between two opposing forces in the form of two distinct receptors (ER alpha and ER beta) and their splice variants. The prospect that these two pathways can be selectively stimulated or inhibited with subtype-selective drugs constitutes new and promising therapeutic opportunities in clinical areas as diverse as hormone replacement, autoimmune diseases, prostate and breast cancer, and depression. Molecular biological, biochemical, and structural studies have generated information which is invaluable for the development of more selective and effective ER ligands. We have also become aware that ERs do not function by themselves but require a number of coregulatory proteins whose cell-specific expression explains some of the distinct cellular actions of estrogen. Estrogen is an important morphogen, and many of its proliferative effects on the epithelial compartment of glands are mediated by growth factors secreted from the stromal compartment. Thus understanding the cross-talk between growth factor and estrogen signaling is essential for understanding both normal and malignant growth. In this review we focus on several of the interesting recent discoveries concerning estrogen receptors, on estrogen as a morphogen, and on the molecular mechanisms of anti-estrogen signaling.
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                Author and article information

                Journal
                Drug Des Devel Ther
                Drug Des Devel Ther
                Drug Design, Development and Therapy
                Drug Design, Development and Therapy
                Dove Medical Press
                1177-8881
                2016
                18 July 2016
                : 10
                : 2323-2331
                Affiliations
                [1 ]Department of Gynecology, the First People’s Hospital of Shangqiu, Shangqiu, Henan, People’s Republic of China
                [2 ]Department of Image Diagnoses, the Third Hospital of Jinan, Jinan, Shandong, People’s Republic of China
                [3 ]Department of Mammary Disease, Guangdong Provincial Hospital of Chinese Medicine, the Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
                Author notes
                Correspondence: Sheng-qi Wang, Department of Mammary Disease, Guangdong Provincial Hospital of Chinese Medicine, the Second Clinical College of Guangzhou University of Chinese Medicine, Dade Road No 111, Guangzhou 510120, People’s Republic of China, Email wsq2011@ 123456126.com
                Article
                dddt-10-2323
                10.2147/DDDT.S110603
                4958355
                27486309
                © 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.

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                Original Research

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