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<p>Breast cancer remains one of the leading cause of cancer related mortality globally. In this study, an integrative in silico approach was applied to design and evaluate novel harmine derivatives as potential anti-breast cancer agents. A data set of 30 harmine derivatives with a known activity against MCF-7 breast cancer cells was used to develop a robust QSAR model using the Genetic Function Approximation method. The selected model showed excellent predictive power with R<sup>2</sup> = 0.9372, Q<sup>2</sup> = 0.8729, and external validation R<sup>2</sup> = 0.7682. Based on the significant descriptors from the QSAR model, ten new compounds were rationally designed. Six of these compounds (NDC1, NDC2, NDC3, NDC7, NDC8, and NDC10) demonstrated improved predictive activity than the template and were subjected to further evaluation. Molecular docking against the estrogen receptor (PDB ID 1UOM) revealed strong binding affinities, with NDC8 displaying the highest moldock score (-133.7&#xa0;kcal/mol). Five out of the six newly designed compounds exhibited stronger binding affinities than the reference drug, Tamoxifen an FDA approved theraphy for breast cancer that targets the estrogen receptor. Molecular dynamics simulations confirmed the structural stability of the top performing complexes over 100&#xa0;ns, with stable RMSF/RMSD values and consistent protein–ligand interactions. DFT study provided insight in to the electronic properties, identifying NDC2 and NDC3 as the most chemically reactive compounds. In silico ADMET analysis showed favorable pharmacokinetic profiles with NDC2, NDC7, NDC8, and NDC10 showing non-toxicity, high absorption, and acceptable excretion and metabolic properties. The study highlights the potential of rational design and computational tools in identifying promising harmine based anti-breast cancer candidates with NDC2 and NDC8 emerging as the most viable leads for future experimental validations.</p>

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Integrative drug discovery approach targeting estrogen receptor: QSAR modelling, molecular docking, molecular dynamics, dft and admet profiling of harmine derivatives

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Sagir Yusuf Ismail,
  • Tasiu Abdulmumin Mikail,
  • Somdutt Mujwar,
  • Bishir Usman

摘要

Breast cancer remains one of the leading cause of cancer related mortality globally. In this study, an integrative in silico approach was applied to design and evaluate novel harmine derivatives as potential anti-breast cancer agents. A data set of 30 harmine derivatives with a known activity against MCF-7 breast cancer cells was used to develop a robust QSAR model using the Genetic Function Approximation method. The selected model showed excellent predictive power with R2 = 0.9372, Q2 = 0.8729, and external validation R2 = 0.7682. Based on the significant descriptors from the QSAR model, ten new compounds were rationally designed. Six of these compounds (NDC1, NDC2, NDC3, NDC7, NDC8, and NDC10) demonstrated improved predictive activity than the template and were subjected to further evaluation. Molecular docking against the estrogen receptor (PDB ID 1UOM) revealed strong binding affinities, with NDC8 displaying the highest moldock score (-133.7 kcal/mol). Five out of the six newly designed compounds exhibited stronger binding affinities than the reference drug, Tamoxifen an FDA approved theraphy for breast cancer that targets the estrogen receptor. Molecular dynamics simulations confirmed the structural stability of the top performing complexes over 100 ns, with stable RMSF/RMSD values and consistent protein–ligand interactions. DFT study provided insight in to the electronic properties, identifying NDC2 and NDC3 as the most chemically reactive compounds. In silico ADMET analysis showed favorable pharmacokinetic profiles with NDC2, NDC7, NDC8, and NDC10 showing non-toxicity, high absorption, and acceptable excretion and metabolic properties. The study highlights the potential of rational design and computational tools in identifying promising harmine based anti-breast cancer candidates with NDC2 and NDC8 emerging as the most viable leads for future experimental validations.