AIMC Topic: Estrogen Receptor alpha

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AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.

Journal of computer-aided molecular design
The integration of artificial intelligence (AI) with molecular modeling offers new opportunities to accelerate therapeutic discovery. In this study, we developed an AI-driven generative pipeline combining deep reinforcement learning (DRL), generative...

Multi-objective QSAR prediction of ERα antagonists via SHAP-based interpretation.

PloS one
To achieve a comprehensive evaluation of candidate drugs in terms of both biological activity and ADMET properties, this study proposes a two-stage predictive framework based on Quantitative Structure-Activity Relationship (QSAR) modeling integrated ...

Selective cytotoxicity of anhydroicaritin in ER-positive breast cancer via ESR1-mediated MAPK and apoptotic signaling.

Chemico-biological interactions
Anhydroicaritin (AHI), a chemically characterized prenylated flavonoid, exhibits strong and selective cytotoxicity against estrogen receptor-positive (ER+) breast cancer cells. In this study, we aimed to elucidate its molecular and cellular toxicolog...

Evaluation of (Z)-endoxifen as a potential therapy for glioblastoma multiforme through computational and experimental analyses.

Scientific reports
(Z)-endoxifen (endoxifen) is the active metabolite of tamoxifen. Endoxifen is a potent antiestrogen that binds and blocks estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ). Early-phase clinical trials have shown that endoxifen has promis...

E2-regulated transcriptome complexity revealed by long-read direct RNA sequencing: from isoform discovery to truncated proteins.

RNA biology
Oestrogen receptor alpha (ERα)-positive (ER+) breast cancers are driven by the binding of 17β-oestradiol (E2) to ERα, which transcriptionally regulates target genes. Although microarrays and conventional RNA sequencing have identified E2 target genes...

Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha.

Nature communications
Target identification in natural products plays a critical role in the development of innovative drugs. Bufalin, a compound derived from traditional medicines, has shown promising anti-cancer activity; however, its precise molecular mechanism of acti...

Integrated Nanopore and short-read RNA sequencing identifies dysregulation of METTL3- m6A modifications in endocrine therapy- sensitive and resistant breast cancer cells.

Functional & integrative genomics
The role of epitranscriptomic changes in the development of acquired endocrine therapy (ET)- resistance in estrogen receptor α (ER) expressing breast cancer (BC) is unknown. We tested the hypothesis that inhibition of METTL3, the methyltransferase re...

Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm.

PloS one
Breast cancer remains a major cause of mortality among women globally, driving the need for advanced therapeutic solutions. This study presents a novel, comprehensive methodology integrating explainable artificial intelligence (AI), machine learning ...

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment.

Environmental pollution (Barking, Essex : 1987)
Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have e...

MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors.

Molecules (Basel, Switzerland)
Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entai...