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. 2025 Jul 1;15(1):21170.
doi: 10.1038/s41598-025-06624-3.

A graph-based computational approach for modeling physicochemical properties in drug design

Ibrahim Al-Dayel  1 , Meraj Ali Khan  1 , Muhammad Faisal Hanif  2 , Muhammad Kamran Siddiqui  3 , Saba Hanif  3 , Brima Gegbe  4
Affiliations

A graph-based computational approach for modeling physicochemical properties in drug design

Ibrahim Al-Dayel et al. Sci Rep. .

Abstract

The efficacy and effectiveness of antibiotics and neuropathic drugs are essentially guided by their physicochemical properties governing stability, bioavailability, and therapeutic activity. This work utilises mathematical modelling and quantitative structure-property relationship (QSPR) analysis for predicting important physicochemical properties such as boiling point, enthalpy of vaporisation, flash point, and molar refraction of chosen antibiotics and neuropathic drugs. Modified degree-based topological indices are utilised as molecular descriptors for correlations between physicochemical functionality and molecular structure. Linear and quadratic forms are various forms of regression models employed for improved predictions. The findings exhibit excellent performance of quadratic models across all but one property compared to linear models, highlighted by significant statistical markers like high [Formula: see text] values and low error margins. These results highlight the potential use of topological descriptors in combination with sound mathematical frameworks for drug optimisation and early-stage screening.

Keywords: Antibiotic compounds; Molecular graph; QSPR analysis; Regression modeling; Topological indices.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
(a) Norfloxacin     (b) Ciprofloxacin    (c) Gemifloxacin.
Fig. 2
Fig. 2
(a) Chlorpyrifos     (b) Dimethoate    (c) Phorate.
Fig. 3
Fig. 3
(a) Monocrotophos     (b) Thiram    (c) Azoxystrobin.
Fig. 4
Fig. 4
(a) Atrazine     (b) Chlorothalonil    (c) Warfarin.
Fig. 5
Fig. 5
Comparison of fits of models for HAC prediction based on formula image.
Fig. 6
Fig. 6
Comparison of fits of models for BP prediction based on formula image.
Fig. 7
Fig. 7
Comparison of fits of models for HAC prediction based on H(G).
Fig. 8
Fig. 8
Comparison of fits of models for HAC prediction based on F(G).
Fig. 9
Fig. 9
Comparison of fits of models for HAC prediction based on SS(G).
Fig. 10
Fig. 10
Comparison of fits of models for HAC prediction based on ABC(G).
Fig. 11
Fig. 11
Comparison of fits of models for HAC prediction based on RI(G).
Fig. 12
Fig. 12
Comparison of fits of models for HAC prediction based on SC(G).
Fig. 13
Fig. 13
Comparison of fits of models for HAC prediction based on GA(G).
Fig. 14
Fig. 14
Comparison of fits of models for HAC prediction based on HZ(G).
Fig. 15
Fig. 15
(a) Comparison of actual and predicted values for BP, (b) Comparison of actual and predicted values for ENV, (c) Comparison of actual and predicted values for FP.
Fig. 16
Fig. 16
(a) Comparison of actual and predicted values for Molar refractivity,    (b) Comparison of actual and predicted values for Polarizability,    (c) Comparison of actual and predicted values for Molar weight.
Fig. 17
Fig. 17
(a) Comparison of actual and predicted values for Heavy atom count,    (b) Comparison of actual and predicted values for Complexity,    (c) Comparison of actual and predicted values for Molar volume.
See this image and copyright information in PMC

References

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