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<p>Topological descriptors from molecular graphs are crucial in quantitative structure–property relationship (QSPR) analysis, linking structure to physicochemical and biological properties. This work studies the detour distance-based index, Detour Eccentric Sum Index (DESI) and the established Eccentric Distance Sum index (EDS) for predictive capabilities. This study explores Quantitative Structure-Property Relationship (QSPR) modeling for SARS-CoV-2 and tuberculosis drugs using novel indices DESI and EDS. Various regression models—logarithmic, exponential, polynomial, and multiple linear—were compared to correlate these topological indices with physicochemical properties. Multicollinearity was assessed through Variance Inflation Factor (VIF) analysis and correlation matrices to ensure model stability. Leave-one-out cross-validation (LOOCV) validated predictions and prevented overfitting in small datasets. For SARS-CoV-2 drugs, second-order DESI models accurately predicted boiling point, enthalpy, and polar surface area; second-order EDS models excelled for polarizability; and logarithmic EDS fitted molar refraction best. In tuberculosis drugs, linear DESI models performed best for molar refraction and polarizability, second-order EDS for molar volume, and combined DESI-EDS multiple linear regression for enthalpy. These distance-based indices, validated through rigorous diagnostics, provide powerful, efficient tools for physicochemical property prediction in computational pharmaceutical design.</p>

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Predictive performance and QSPR analysis of SARS-CoV-2 and tuberculosis drugs using distance-based topological descriptors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Supriya,
  • Radha Rajamani Iyer

摘要

Topological descriptors from molecular graphs are crucial in quantitative structure–property relationship (QSPR) analysis, linking structure to physicochemical and biological properties. This work studies the detour distance-based index, Detour Eccentric Sum Index (DESI) and the established Eccentric Distance Sum index (EDS) for predictive capabilities. This study explores Quantitative Structure-Property Relationship (QSPR) modeling for SARS-CoV-2 and tuberculosis drugs using novel indices DESI and EDS. Various regression models—logarithmic, exponential, polynomial, and multiple linear—were compared to correlate these topological indices with physicochemical properties. Multicollinearity was assessed through Variance Inflation Factor (VIF) analysis and correlation matrices to ensure model stability. Leave-one-out cross-validation (LOOCV) validated predictions and prevented overfitting in small datasets. For SARS-CoV-2 drugs, second-order DESI models accurately predicted boiling point, enthalpy, and polar surface area; second-order EDS models excelled for polarizability; and logarithmic EDS fitted molar refraction best. In tuberculosis drugs, linear DESI models performed best for molar refraction and polarizability, second-order EDS for molar volume, and combined DESI-EDS multiple linear regression for enthalpy. These distance-based indices, validated through rigorous diagnostics, provide powerful, efficient tools for physicochemical property prediction in computational pharmaceutical design.