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Volume 22, Issue 1
  • ISSN: 1570-1638
  • E-ISSN: 1875-6220

Abstract

Background

The significant public health effect of breast cancer is demonstrated by its high global prevalence and the potential for severe health consequences. The suppression of the proliferative effects facilitated by the estrogen receptor alpha (ERα) in the MCF-7 cell line is significant for breast cancer therapy.

Objective

The current work involves techniques for identifying potential inhibitors of ERα.

Methods

The method combines QSAR models based on machine learning with molecular docking to identify potential binders for the ERα. Further, molecular dynamics simulation studied the stability of the complexes, and ADMET analysis validated the compound’s properties.

Results

Two compounds ( and ) showed significant binding affinities with ERα, with binding energies comparable to the established binder RL4. The ADMET qualities showed advantageous characteristics resembling pharmaceutical drugs. The stable binding of these ligands in the active region of ERα during dynamic conditions was confirmed by molecular dynamics simulations. RMSD plots and conformational stability supported the ligands' persistent occupancy in the protein's binding site. After simulation, two hydrogen bonds were found within the protein-ligand complexes of and , with binding free energy values of -27.32 kcal/mol and -25.00 kcal/mol.

Conclusion

The study suggests that compounds and could be useful for developing innovative anti-ERα medicines. However, more research is needed to prove the compounds' breast cancer treatment efficacy. This will help develop new treatments for ERα-associated breast cancer.

© 2024 Bentham Science Publishers
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Repurposing Phytochemicals against Breast Cancer (MCF-7) using Classical Structure-Based Drug Design
Current Drug Discovery Technologies 22, E280324228430 (2025); https://doi.org/10.2174/0115701638295736240315105737
/content/journals/cddt/10.2174/0115701638295736240315105737
/content/journals/cddt/10.2174/0115701638295736240315105737
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  • Article Type: Research Article
Keyword(s): breast cancer; Estrogen receptor alpha (ERα); hydrogen bond; MCF-7 cell line; molecular dynamics simulations; quantitative structure-activity relationship (QSAR) model

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