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<p>Topological descriptors are quantitative parameters that correlate the attributes of molecular structures, providing insights into their chemical properties and behavior. With the increasing significance of dendrimers in drug design, driven by advancements in technology, our study focuses on the topological analysis of non-conjugated ethylene oxide cored dendrimer decorated with tetraphenylethylene. We derive key degree-based descriptors, including degree, neighborhood degree, and reverse degree, using the novel M-polynomial approach. This innovative method not only facilitates the calculation of these descriptors but also has the potential to generate additional descriptors. Our results demonstrate the applicability of these descriptors in Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models, predicting properties across different generations of the dendrimer. Furthermore, a graphical comparison is provided to enhance the understanding and analysis of the derived descriptors. This work represents a significant step forward in the mathematical modeling of dendritic structures, offering new tools for researchers in the field of chemical graph theory and molecular chemistry.</p> Graphical abstract <p>This study presents a computational framework for predicting molecular properties without experimental research. Molecular descriptors, including degree based and entropy measures, are utilized in QSPR analysis. Statistical tools and regression models facilitate property prediction, offering valuable insights into molecular structures and their characteristics through topological descriptors.</p>

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Topological analysis of non-conjugated ethylene oxide cored dendrimers decorated with tetraphenylethylene: Insights from degree-based descriptors using the polynomial approach

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • A Theertha Nair,
  • D Antony Xavier,
  • Annmaria Baby,
  • S Akhila

摘要

Topological descriptors are quantitative parameters that correlate the attributes of molecular structures, providing insights into their chemical properties and behavior. With the increasing significance of dendrimers in drug design, driven by advancements in technology, our study focuses on the topological analysis of non-conjugated ethylene oxide cored dendrimer decorated with tetraphenylethylene. We derive key degree-based descriptors, including degree, neighborhood degree, and reverse degree, using the novel M-polynomial approach. This innovative method not only facilitates the calculation of these descriptors but also has the potential to generate additional descriptors. Our results demonstrate the applicability of these descriptors in Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models, predicting properties across different generations of the dendrimer. Furthermore, a graphical comparison is provided to enhance the understanding and analysis of the derived descriptors. This work represents a significant step forward in the mathematical modeling of dendritic structures, offering new tools for researchers in the field of chemical graph theory and molecular chemistry.

Graphical abstract

This study presents a computational framework for predicting molecular properties without experimental research. Molecular descriptors, including degree based and entropy measures, are utilized in QSPR analysis. Statistical tools and regression models facilitate property prediction, offering valuable insights into molecular structures and their characteristics through topological descriptors.