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<p>This paper shows a graph-based exploration of the structure-property correlation among some specific phytochemicals in terms of matching energy as a topological measure. The individual molecules are modeled as graphs, and the corresponding polynomials are calculated to obtain corresponding energy as a topological characteristic. A sample of varied plant-based bioactive substances is examined to examine the connection between matching energy and various physicochemical characteristics, such as molecular weight (MW), molar refractivity (MR), Bertz complexity (BertzCT), NHA, Heavy Atoms, Chi 0v, Chi 1v, and Labute ASA. The high correlations between matching energy and all the properties under study showed that linear regression analysis is a good predictive parameter. To achieve greater predictive accuracy, the use of the XGBoost regression model is employed, which produced greater results in terms of high values of the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(r^{2}\)</EquationSource> </InlineEquation> and low error in prediction. Train-test validation, k-fold cross-validation, and bootstrap analysis are used to test the reliability of the models. The findings, in general, underscore the usefulness of the integration of both graph theory and machine learning to precisely predict the physicochemical properties of phytochemical compounds.</p>

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A graph-theoretical and machine learning framework for structure–property analysis of phytochemicals via matching energy

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Sadia Noureen,
  • Nazma Ashraf,
  • Majid Hussain

摘要

This paper shows a graph-based exploration of the structure-property correlation among some specific phytochemicals in terms of matching energy as a topological measure. The individual molecules are modeled as graphs, and the corresponding polynomials are calculated to obtain corresponding energy as a topological characteristic. A sample of varied plant-based bioactive substances is examined to examine the connection between matching energy and various physicochemical characteristics, such as molecular weight (MW), molar refractivity (MR), Bertz complexity (BertzCT), NHA, Heavy Atoms, Chi 0v, Chi 1v, and Labute ASA. The high correlations between matching energy and all the properties under study showed that linear regression analysis is a good predictive parameter. To achieve greater predictive accuracy, the use of the XGBoost regression model is employed, which produced greater results in terms of high values of the \(r^{2}\) and low error in prediction. Train-test validation, k-fold cross-validation, and bootstrap analysis are used to test the reliability of the models. The findings, in general, underscore the usefulness of the integration of both graph theory and machine learning to precisely predict the physicochemical properties of phytochemical compounds.