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<p>This study investigates the efficacy of eight multiplicative degree-based and three classical degree-based topological indices in Quantitative Structure-Property Relationship (QSPR) models for predicting critical physicochemical properties of 38 antidepressant drugs. Molecular structures of compounds including Bupropion, Amitriptyline, and Fluoxetine were translated into numerical descriptors using indices such as Multiplicative Sum Zagreb, Multiplicative Sombor, and the First Zagreb index. These descriptors were integrated with machine learning algorithms: Random Forest, XGBoost, and linear regression to forecast boiling points, melting points, critical temperature, critical volume, and molar refractivity. Results revealed that the XGBoost algorithm significantly outperformed other methods, achieving superior predictive accuracy with the lowest error metrics (e.g., for boiling point: MAE = 8.60, RMSE = 12.40, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(r^2= 0.995\)</EquationSource> </InlineEquation>). Among the topological indices, the First Zagreb index (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(M_1(G)\)</EquationSource> </InlineEquation>) emerged as the most robust descriptor, demonstrating the strongest correlations with key properties (e.g., <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(r = 0.932\)</EquationSource> </InlineEquation> with critical volume, <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(r = 0.938\)</EquationSource> </InlineEquation> with molar refractivity). Linear regression models further confirmed the significance of <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(M_1(G)\)</EquationSource> </InlineEquation> and other indices, with high statistical significance (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(p &lt; 0.05\)</EquationSource> </InlineEquation>) in most cases. This interdisciplinary approach demonstrates the potent synergy of graph-theoretic indices and advanced machine learning in pharmaceutical research, offering a powerful strategy to accelerate drug discovery and optimize the design of novel therapeutic agents.</p>

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Leveraging topological indices and machine learning for advanced prediction of antidepressant drug properties

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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  • Guoping Zhang,
  • Sadia Noureen,
  • Saood Azam,
  • Manal Elzain Mohamed Abdalla,
  • Mohammed E. Dafaalla,
  • Adnan Aslam,
  • Keneni Abera Tola

摘要

This study investigates the efficacy of eight multiplicative degree-based and three classical degree-based topological indices in Quantitative Structure-Property Relationship (QSPR) models for predicting critical physicochemical properties of 38 antidepressant drugs. Molecular structures of compounds including Bupropion, Amitriptyline, and Fluoxetine were translated into numerical descriptors using indices such as Multiplicative Sum Zagreb, Multiplicative Sombor, and the First Zagreb index. These descriptors were integrated with machine learning algorithms: Random Forest, XGBoost, and linear regression to forecast boiling points, melting points, critical temperature, critical volume, and molar refractivity. Results revealed that the XGBoost algorithm significantly outperformed other methods, achieving superior predictive accuracy with the lowest error metrics (e.g., for boiling point: MAE = 8.60, RMSE = 12.40, \(r^2= 0.995\) ). Among the topological indices, the First Zagreb index ( \(M_1(G)\) ) emerged as the most robust descriptor, demonstrating the strongest correlations with key properties (e.g., \(r = 0.932\) with critical volume, \(r = 0.938\) with molar refractivity). Linear regression models further confirmed the significance of \(M_1(G)\) and other indices, with high statistical significance ( \(p < 0.05\) ) in most cases. This interdisciplinary approach demonstrates the potent synergy of graph-theoretic indices and advanced machine learning in pharmaceutical research, offering a powerful strategy to accelerate drug discovery and optimize the design of novel therapeutic agents.