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

Abstract

BackgroundEstrogen 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.MethodsHerein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods.ResultsThe 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.ConclusionThese 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.

Fructus Ligustri Lucidi modulates estrogen receptor expression with no uterotrophic effect in ovariectomized rats

“…As such, classical hormone replacement therapy (HRT) is widely used to prevent both menopausal symptoms and osteoporotic fractures [ 3 ]. Estrogen promotes bone accrual through estrogen receptor (ER) α and ERβ [ 4 ]. In addition, deletion of ERα in female mice exhibits reduction of bone mass and strength [ 5 , 6 ], whereas increased expression of ERα in endothelium is associated with risk of developing breast and uterine cancer, which are also main side effects induced by HRT treatment [ 7 , 8 ].…”

“…In addition, deletion of ERα in female mice exhibits reduction of bone mass and strength [ 5 , 6 ], whereas increased expression of ERα in endothelium is associated with risk of developing breast and uterine cancer, which are also main side effects induced by HRT treatment [ 7 , 8 ]. Moreover, selective activation of ERβ contributes to inhibition of breast cell proliferation and is also one of optimal targets to elicit beneficial estrogen-like activities [ 4 , 9 , 10 ]. Therefore, discovery and development of selective ER agonist remains a need for osteoporosis treatment.…”

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

“…In silico computational approaches such as machine learning (ML) methods are useful tools for discovery agonists and antagonists, particularly in modeling of ligand-binding protein activation with an increasing number of new chemical compounds synthesized (Banerjee et al, 2016;Niu et al, 2016;Asako and Uesawa, 2017;Wink et al, 2018;Bitencourt-Ferreira and de Azevedo, 2019;Da'adoosh et al, 2019;Kim G. B. et al, 2019). Among in silico approaches, both qualitative classification and quantitative prediction models by quantitative structureactivity relationship (QSAR) methods were reported using a large collection of environmental chemicals (Zang et al, 2013;Niu et al, 2016;Norinder and Boyer, 2016;Cotterill et al, 2019;Dreier et al, 2019;Heo et al, 2019). However, building highperformance prediction model requires specialized techniques, such as selecting appropriate features and algorithms (Beltran et al, 2018;Khan and Roy, 2018).…”

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations

“…Those structures are useful to understand the structural changes due to agonist and antagonist binding in the ERα LBP. Various computational techniques such as molecular docking [18][19][20][21][22][23][24], MD simulations [25][26][27][28][29][30], predictive modeling [31][32][33][34][35][36][37][38][39][40][41], and in vitro studies were conducted to predict ER binders or non-binders [42,43] and agonists or antagonists [44,45].…”