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<p>Cancer is an ongoing severe health complication and public health concern. Efforts have been made to overcome this; however, the emergence of multidrug resistance (MDR) remains a major hurdle for available therapies, limiting their effectiveness. Therefore, in this study, an AI-integrated computational framework was employed to identify a promising compound from <i>Camellia sinensis</i> targeting <i>P-glycoprotein</i> and enhance its binding affinity based on dynamic insights. <i>Camellia sinensis</i>, also known as green tea, is one of the well-known plants for its anticancer properties. A total of 209 compounds from <i>Camellia sinensis</i> were curated and screened, of which 32 compounds were prioritized based on the drug-likeness screening and selected for molecular docking analysis. Among the screened compounds, quercetin exhibited the highest docking score toward P-glycoprotein, with a docking score of − 7.751&#xa0;kcal/mol. Subsequently, quercetin was further optimized and enhanced through the AI-based approach, resulting in five derivatives, among which Q5 was found to be most suitable and had the enhanced docking score compared to the parent compound, i.e., − 7.832&#xa0;kcal/mol. The ADME analysis of both compounds was performed, and the AI-derived lead showed more promising drug-like properties than quercetin. Additionally, the molecular dynamics simulation was performed for 500&#xa0;ns, along with post-simulation analysis, including RMSD, RMSF, PCA, and MMGBSA. The analysis demonstrated the minimal RMSD fluctuations and maintained conformational integrity, along with retention of binding stability with a moderate redistribution throughout the simulation period, showing the complex’s reliable stability. Additionally, the DFT analysis was accomplished to examine the electronic properties of the AI-derived lead (Q5). Collectively, based on the computational analysis, the study demonstrates that the AI-enhanced quercetin derivative, Q5, is promising and can serve as a potential lead compound. However, the experimental evaluation is required to ensure the efficacy and safety of the AI-enhanced lead.</p>

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Quercetin derivatives from Camellia sinensis for the discovery of anticancer compound against P-glycoprotein-mediated multidrug resistance: a computational approach

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Saurav Kumar Mishra,
  • Turki Alfuhayr,
  • Akansha Subba,
  • Noimul Hasan Siddiquee,
  • Shahadul Hassan Sourav,
  • Umme Hani,
  • Magdi E. A. Zaki,
  • John J. Georrge

摘要

Cancer is an ongoing severe health complication and public health concern. Efforts have been made to overcome this; however, the emergence of multidrug resistance (MDR) remains a major hurdle for available therapies, limiting their effectiveness. Therefore, in this study, an AI-integrated computational framework was employed to identify a promising compound from Camellia sinensis targeting P-glycoprotein and enhance its binding affinity based on dynamic insights. Camellia sinensis, also known as green tea, is one of the well-known plants for its anticancer properties. A total of 209 compounds from Camellia sinensis were curated and screened, of which 32 compounds were prioritized based on the drug-likeness screening and selected for molecular docking analysis. Among the screened compounds, quercetin exhibited the highest docking score toward P-glycoprotein, with a docking score of − 7.751 kcal/mol. Subsequently, quercetin was further optimized and enhanced through the AI-based approach, resulting in five derivatives, among which Q5 was found to be most suitable and had the enhanced docking score compared to the parent compound, i.e., − 7.832 kcal/mol. The ADME analysis of both compounds was performed, and the AI-derived lead showed more promising drug-like properties than quercetin. Additionally, the molecular dynamics simulation was performed for 500 ns, along with post-simulation analysis, including RMSD, RMSF, PCA, and MMGBSA. The analysis demonstrated the minimal RMSD fluctuations and maintained conformational integrity, along with retention of binding stability with a moderate redistribution throughout the simulation period, showing the complex’s reliable stability. Additionally, the DFT analysis was accomplished to examine the electronic properties of the AI-derived lead (Q5). Collectively, based on the computational analysis, the study demonstrates that the AI-enhanced quercetin derivative, Q5, is promising and can serve as a potential lead compound. However, the experimental evaluation is required to ensure the efficacy and safety of the AI-enhanced lead.