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

ABSTRACT: 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 benefits from the usual treatments owing to the innate resistance mechanisms, while 30-40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-t

SUBMITTER: Schaduangrat N 

PROVIDER: S-EPMC10752908 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature