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<p>Topological indices are an important part of chemical graph theory as a representation of the molecular structure and a predictor of the physicochemical properties. In this paper, nine neighborhood degree sum based topological indices are use as indicators of predictive potential in a QSPR analysis of twenty antifungal drug molecules. Linear, quadratic, and cubic regression models were used to test the relationships between the structural properties and important physicochemical properties, such as boiling point, density, enthalpy of vaporization, flash point, refractive index, molar refractivity, polarizability, surface tension, and molar volume. The findings also revealed that a cubic regression model provided the most optimal overall performance with the highest level of predictability in the form of molar refractivity and polarizability being the neighborhood inverse product index <InlineEquation ID="IEq1"><EquationSource Format="TEX">\((ND_{4} )\)</EquationSource></InlineEquation> (<InlineEquation ID="IEq447"><EquationSource Format="TEX">\(R^{2}\)</EquationSource></InlineEquation>&#xa0;=&#xa0;0.98). These results assert that neighborhood based topological descriptors are effective, especially in modeling the refractivity related and electronic properties of drug molecules.</p>

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Predicting properties of antifungal drug molecules using neighborhood degree topological indices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Abdullah Ahmed Almulla,
  • Ibrahim Irfan,
  • Zeeshan Saleem Mufti,
  • Mazen Omar Almulla,
  • Gamachu Adugna Ganati

摘要

Topological indices are an important part of chemical graph theory as a representation of the molecular structure and a predictor of the physicochemical properties. In this paper, nine neighborhood degree sum based topological indices are use as indicators of predictive potential in a QSPR analysis of twenty antifungal drug molecules. Linear, quadratic, and cubic regression models were used to test the relationships between the structural properties and important physicochemical properties, such as boiling point, density, enthalpy of vaporization, flash point, refractive index, molar refractivity, polarizability, surface tension, and molar volume. The findings also revealed that a cubic regression model provided the most optimal overall performance with the highest level of predictability in the form of molar refractivity and polarizability being the neighborhood inverse product index \((ND_{4} )\) (\(R^{2}\) = 0.98). These results assert that neighborhood based topological descriptors are effective, especially in modeling the refractivity related and electronic properties of drug molecules.