In cheminformatics, topological indices serve as essential descriptors in quantitative structure–property relationship (QSPR) modeling to estimate molecular characteristics of pharmaceutical compounds. This work investigates the predictive capabilities of degree-based and neighborhood degree-based topological indices through established M-polynomial and NM-polynomial techniques on a set of nine cardiovascular antihypertensive agents. Linear regression models revealed statistically significant correlations between selected indices and key physicochemical properties. Notably, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12668_2025_2088_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="49" /> </InlineMediaObject> <EquationSource Format="TEX">$TMH$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">TMH</mi> </mrow> </math></EquationSource> </InlineEquation> showed robust predictive strength for boiling point (r = 0.8358), enthalpy (r = 0.8618), and flash point (r = 0.8357), while the <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12668_2025_2088_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$H$</EquationSource> <EquationSource Format="MATHML"><math> <mi>H</mi> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12668_2025_2088_Article_IEq3.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">${ND}_{5}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="italic">ND</mi> </mrow> <mn>5</mn> </msub> </math></EquationSource> </InlineEquation>, and <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12668_2025_2088_Article_IEq4.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">${M}_{2}^{m}$</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>M</mi> <mrow> <mn>2</mn> </mrow> <mi>m</mi> </msubsup> </math></EquationSource> </InlineEquation> indices yielded near-perfect correlations with molar refractivity (r = 0.9977), polarizability (r = 0.9976), and molar volume (r = 0.9915), respectively. These findings confirm the utility of select topological indices in capturing structure–property relationships. These results demonstrate the potential of degree-based topological descriptors as reliable predictive tools in cheminformatics-driven QSPR/QSAR modeling, offering a robust and cost-effective approach for early-stage computational drug evaluation. Graphical abstract

Degree-Based Topological Indices for QSPR/QSAR Modelling of Cardiovascular Antihypertensive Agents Using Statistical Regression Techniques

期刊论文
发表日期：

2025年8月27日

查看全文

Pradeepa A,
Arathi P

摘要

In cheminformatics, topological indices serve as essential descriptors in quantitative structure–property relationship (QSPR) modeling to estimate molecular characteristics of pharmaceutical compounds. This work investigates the predictive capabilities of degree-based and neighborhood degree-based topological indices through established M-polynomial and NM-polynomial techniques on a set of nine cardiovascular antihypertensive agents. Linear regression models revealed statistically significant correlations between selected indices and key physicochemical properties. Notably, $TMH$ $TMH$ showed robust predictive strength for boiling point (r = 0.8358), enthalpy (r = 0.8618), and flash point (r = 0.8357), while the $H$ $H$ , ${ND}_{5}$ ${ND}_{5}$ , and ${M}_{2}^{m}$ $M_{2}^{m}$ indices yielded near-perfect correlations with molar refractivity (r = 0.9977), polarizability (r = 0.9976), and molar volume (r = 0.9915), respectively. These findings confirm the utility of select topological indices in capturing structure–property relationships. These results demonstrate the potential of degree-based topological descriptors as reliable predictive tools in cheminformatics-driven QSPR/QSAR modeling, offering a robust and cost-effective approach for early-stage computational drug evaluation.

Graphical abstract