StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

N Schaduangrat, N Homdee, W Shoombuatong - Scientific Reports, 2023 - nature.com
The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical
practice and scientific exploration. However, around 15–30% of those affected do not see …

In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method

J Xu, Z Huang, H Duan, W Li, J Zhuang, L Xiong… - ChemMedChem - Wiley Online Library
Estrogen‐related receptor α (ERRα) is considered a very promising target for treating
metabolic diseases such as type 2 diabetes. Development of a prediction model to quickly …

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug …

T Turki, Y Taguchi - Mathematics, 2024 - mdpi.com
Understanding breast cancer drug response mechanisms can play a crucial role in
improving treatment outcomes and survival rates. Existing bioinformatics-based approaches …

Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches

X Pang, W Fu, J Wang, D Kang, L Xu… - Oxidative medicine …, 2018 - Wiley Online Library
Estrogen receptor α (ERα) is a successful target for ER‐positive breast cancer and also
reported to be relevant in many other diseases. Selective estrogen receptor modulators …

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

A Niu, L Xie, H Wang, B Zhu, S Wang - Drug Design, Development …, 2016 - Taylor & Francis
Background Estrogen receptors (ERs) are nuclear transcription factors that are involved in
the regulation of many complex physiological processes in humans. ERs have been …

A systematic in silico mining of the mechanistic implications and therapeutic potentials of estrogen receptor (ER)-α in breast cancer

X Li, R Sun, W Chen, B Lu, X Li, Z Wang, J Bao - Plos One, 2014 - journals.plos.org
Estrogen receptor (ER)-α has long been a potential target in ER-α-positive breast cancer
therapeutics. In this study, we integrated ER-α-related bioinformatic data at different levels to …

[HTML][HTML] Discovery of novel selective ERα/ERβ ligands by multi-pharmacophore modeling and virtual screening

W Huang, W Wei, Y Yang, T Zhang… - Chemical and …, 2015 - jstage.jst.go.jp
Estrogen receptor α (ERα) and estrogen receptor β (ERβ) regulate different sets of gene
expression, and have different ligand responses, which make the estrogen tissue-specific …

A machine learning-based approach to ERα bioactivity and drug ADMET prediction

T An, Y Chen, Y Chen, L Ma, J Wang, J Zhao - Frontiers in Genetics, 2023 - frontiersin.org
By predicting ERα bioactivity and mining the potential relationship between Absorption,
Distribution, Metabolism, Excretion, Toxicity (ADMET) attributes in drug research and …

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-kB receptors based on machine learning and molecular docking

P Rezaee, S Rezaee, M Maaza, SS Arab - arXiv preprint arXiv:2405.00647, 2024 - arxiv.org
Breast cancer, the second most prevalent cancer among women worldwide, necessitates
the exploration of novel therapeutic approaches. To target the four subgroups of breast …

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

N Schaduangrat, N Anuwongcharoen, MA Moni… - Scientific Reports, 2022 - nature.com
Progesterone receptors (PRs) are implicated in various cancers since their
presence/absence can determine clinical outcomes. The overstimulation of progesterone …