title: Prediction of selective estrogen receptor beta agonist using open data and machine learning approach.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Gynecology, the First People's Hospital of Shangqiu, Shangqiu, Henan, People's Republic of China.
authors.forename: Ai-Qin
authors.initials: AQ
authors.lastname: Niu
authors.affiliation: Department of Image Diagnoses, the Third Hospital of Jinan, Jinan, Shandong, People's Republic of China.
authors.forename: Liang-Jun
authors.initials: LJ
authors.lastname: Xie
authors.affiliation: Department of Gynecology, the First People's Hospital of Shangqiu, Shangqiu, Henan, People's Republic of China.
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Gynecology, the First People's Hospital of Shangqiu, Shangqiu, Henan, People's Republic of China.
authors.forename: Bing
authors.initials: B
authors.lastname: Zhu
authors.affiliation: 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.
authors.forename: Sheng-Qi
authors.initials: SQ
authors.lastname: Wang
mesh_terms: D003198:Computer Simulation; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D047629:Estrogen Receptor beta / Q000819:agonists*; D006801:Humans; D000069550:Machine Learning; D066295:Open Access Publishing; D021281:Quantitative Structure-Activity Relationship
chemical_list: D047629:Estrogen Receptor beta
keywords: estrogen receptor subtype β; machine learning approach; quantitative structure-activity relationship models; selective estrogen receptor modulators