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<p>Pesticides are vital for global agriculture and public health, offering protection against pests that threaten crops and livestock. The efficacy and safety of these compounds are heavily influenced by their physicochemical properties. This study utilizes classical degree based and eigenvalue-based topological indices to develop predictive models for four key properties—complexity, molecular weight, XlogP, and topological polar surface area—across 34 commonly used pesticides. Using linear regression, topological indices were employed as independent variables to estimate these properties. The results demonstrate strong predictive performance, particularly for molecular weight (R<sup>2</sup> = 0.939, <i>p</i> &lt; 0.001) and complexity (R<sup>2</sup> = 0.912, <i>p</i> &lt; 0.001), with the Randić index and atom-bond connectivity energy emerging as the most effective descriptors. Validation on compounds such as Terbutryn and Tetramethrin confirmed the models’ accuracy, with predicted values closely aligning with experimental data. These findings highlight the potential of topological indices as efficient computational tools for predicting essential molecular properties, supporting the rational design and application of pesticides.</p>

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Topological indices as predictors of physicochemical properties in pesticides

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Yanxia Fu,
  • Baoyang Dong

摘要

Pesticides are vital for global agriculture and public health, offering protection against pests that threaten crops and livestock. The efficacy and safety of these compounds are heavily influenced by their physicochemical properties. This study utilizes classical degree based and eigenvalue-based topological indices to develop predictive models for four key properties—complexity, molecular weight, XlogP, and topological polar surface area—across 34 commonly used pesticides. Using linear regression, topological indices were employed as independent variables to estimate these properties. The results demonstrate strong predictive performance, particularly for molecular weight (R2 = 0.939, p < 0.001) and complexity (R2 = 0.912, p < 0.001), with the Randić index and atom-bond connectivity energy emerging as the most effective descriptors. Validation on compounds such as Terbutryn and Tetramethrin confirmed the models’ accuracy, with predicted values closely aligning with experimental data. These findings highlight the potential of topological indices as efficient computational tools for predicting essential molecular properties, supporting the rational design and application of pesticides.