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<p>Estrogen receptor alpha (ERα) is the key mediator of estrogen receptor-positive (ER+) breast carcinoma; the most common form of breast malignancy and the principal target of existing medications against the disease. Due to the declining efficacy of the existing drugs against ER+ breast cancer, it has become expedient to search for newer and better options. Herein, we applied integrated in silico drug discovery techniques, including 2D-QSAR modeling, molecular docking, MM/GBSA calculations, ADMET profiling, molecular dynamics, and DFT calculations to prioritize pyrazolochalcone-based ERα antagonists. The optimal QSAR model achieved R<sup>2</sup><sub>train</sub> = 0.946, R<sup>2</sup><sub>adj</sub> = 0.934, Q<sup>2</sup> = 0.917, and <sup>c</sup>R<sub>p</sub><sup>2</sup> = 0.888. In addition, the model displays sound external predictive ability (R<sup>2</sup><sub>Test</sub> = 0.725, k = 1.024, k’ = 0.974, and CCC = 0.73). Among the designed ligands, PC-2 demonstrated the most promising characteristics to be prioritized as a lead molecule in the search for potent and less toxic antagonists of ERα for breast cancer intervention. The ligand showed the strongest binding interactions with the receptor (∆G = −&#xa0;8.8&#xa0;kcal/mol and ∆G<sub>T</sub> = −&#xa0;55.04&#xa0;kcal/mol), good gastrointestinal absorption (GIA) potential (≈ 70%), negative AMES status, and a non-inhibitor of hERG1. Also, 100 ns MD simulations confirmed a stable binding pose. While the findings of this in silico study highlights PC-2 as a priority candidate for synthesis and experimental evaluation, confirmation of its potential advantages over tamoxifen will require further in vitro and in vivo validation.</p>

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In silico insights into pyrazolochalcones: molecular modeling and design of novel antagonists of estrogen receptor for breast cancer intervention using QSAR modeling, molecular docking, molecular dynamics, DFT, and ADMET studies

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Philip John Ameji,
  • Amneh Shtaiwi,
  • Rohana Adnan

摘要

Estrogen receptor alpha (ERα) is the key mediator of estrogen receptor-positive (ER+) breast carcinoma; the most common form of breast malignancy and the principal target of existing medications against the disease. Due to the declining efficacy of the existing drugs against ER+ breast cancer, it has become expedient to search for newer and better options. Herein, we applied integrated in silico drug discovery techniques, including 2D-QSAR modeling, molecular docking, MM/GBSA calculations, ADMET profiling, molecular dynamics, and DFT calculations to prioritize pyrazolochalcone-based ERα antagonists. The optimal QSAR model achieved R2train = 0.946, R2adj = 0.934, Q2 = 0.917, and cRp2 = 0.888. In addition, the model displays sound external predictive ability (R2Test = 0.725, k = 1.024, k’ = 0.974, and CCC = 0.73). Among the designed ligands, PC-2 demonstrated the most promising characteristics to be prioritized as a lead molecule in the search for potent and less toxic antagonists of ERα for breast cancer intervention. The ligand showed the strongest binding interactions with the receptor (∆G = − 8.8 kcal/mol and ∆GT = − 55.04 kcal/mol), good gastrointestinal absorption (GIA) potential (≈ 70%), negative AMES status, and a non-inhibitor of hERG1. Also, 100 ns MD simulations confirmed a stable binding pose. While the findings of this in silico study highlights PC-2 as a priority candidate for synthesis and experimental evaluation, confirmation of its potential advantages over tamoxifen will require further in vitro and in vivo validation.