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<p>This study computes M-polynomial indices for Daunorubicin, ananthracycline antibiotic, is a potent anticancer agent used in treating variousmalignancies, including acute myeloid leukemia, acute lymphoblastic leukemia andbreast cancer. We calculated M-polynomial indices using the edge partition of graphsbased on degree and adjacency matrix. A Python code is developed based on anadjacency matrix to efficiently compute the indices that reduce calculation timefrom days to minutes and eliminate human error. Quantitative structure-propertyrelationships are established using Multiple Linear, Ridge, Lasso, ElasticNet andSupport Vector Regression in Python software to predict breast cancer drugs’physical properties. Our results demonstrate that M-polynomial indices accuratelypredict physical properties, providing valuable insights into structuralrequirements for optimal anticancer activity. Additionally, we proposed the modelsagainst each physical property. This research facilitates the design of novel cancertherapeutics and enables the prediction of physical properties for uncharacterizeddrugs.</p>

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RETRACTED ARTICLE: Mathematical modeling and statistical analysis of breast cancer drugs using M-polynomial indices for the physical properties

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Qasem M. Tawhari,
  • Muhammad Naeem,
  • Saba Maqbool,
  • Abdul Rauf,
  • Oladele Oyelakin

摘要

This study computes M-polynomial indices for Daunorubicin, ananthracycline antibiotic, is a potent anticancer agent used in treating variousmalignancies, including acute myeloid leukemia, acute lymphoblastic leukemia andbreast cancer. We calculated M-polynomial indices using the edge partition of graphsbased on degree and adjacency matrix. A Python code is developed based on anadjacency matrix to efficiently compute the indices that reduce calculation timefrom days to minutes and eliminate human error. Quantitative structure-propertyrelationships are established using Multiple Linear, Ridge, Lasso, ElasticNet andSupport Vector Regression in Python software to predict breast cancer drugs’physical properties. Our results demonstrate that M-polynomial indices accuratelypredict physical properties, providing valuable insights into structuralrequirements for optimal anticancer activity. Additionally, we proposed the modelsagainst each physical property. This research facilitates the design of novel cancertherapeutics and enables the prediction of physical properties for uncharacterizeddrugs.