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

ABSTRACT: 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 (SERMs) make a good therapeutic effect in clinic. Because of the drug resistance and side effects of current SERMs, the discovery of new SERMs is given more and more attention. Virtual screening is a validated method to high effectively to identify novel bioactive small molecules. Ligand-based machine learning methods and structure-based molecular docking were first performed for identification of ERα antagonist from in-house natural product library. Naive Bayesian and recursive partitioning models with two kinds of descriptors were built and validated based on training set, test set, and external

SUBMITTER: Pang X 

PROVIDER: S-EPMC5971309 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature