A graph-based computational approach for modeling physicochemical properties in drug design
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) an...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-06624-3 |
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| author | Ibrahim Al-Dayel Meraj Ali Khan Muhammad Faisal Hanif Muhammad Kamran Siddiqui Saba Hanif Brima Gegbe |
| author_facet | Ibrahim Al-Dayel Meraj Ali Khan Muhammad Faisal Hanif Muhammad Kamran Siddiqui Saba Hanif Brima Gegbe |
| author_sort | Ibrahim Al-Dayel |
| collection | DOAJ |
| description | 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 $$R^{2}$$ 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. |
| format | Article |
| id | doaj-art-b93356b6d5e64f35a77d2e5b785c2255 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b93356b6d5e64f35a77d2e5b785c22552025-08-20T03:03:33ZengNature PortfolioScientific Reports2045-23222025-07-0115112210.1038/s41598-025-06624-3A graph-based computational approach for modeling physicochemical properties in drug designIbrahim Al-Dayel0Meraj Ali Khan1Muhammad Faisal Hanif2Muhammad Kamran Siddiqui3Saba Hanif4Brima Gegbe5Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, The University of Lahore, Lahore CampusDepartment of Mathematics, COMSATS University Islamabad, Lahore CampusDepartment of Mathematics, COMSATS University Islamabad, Lahore CampusDepartment of Mathematics and Statistics, Njala UniversityAbstract 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 $$R^{2}$$ 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.https://doi.org/10.1038/s41598-025-06624-3Molecular graphQSPR analysisAntibiotic compoundsTopological indicesRegression modeling |
| spellingShingle | Ibrahim Al-Dayel Meraj Ali Khan Muhammad Faisal Hanif Muhammad Kamran Siddiqui Saba Hanif Brima Gegbe A graph-based computational approach for modeling physicochemical properties in drug design Scientific Reports Molecular graph QSPR analysis Antibiotic compounds Topological indices Regression modeling |
| title | A graph-based computational approach for modeling physicochemical properties in drug design |
| title_full | A graph-based computational approach for modeling physicochemical properties in drug design |
| title_fullStr | A graph-based computational approach for modeling physicochemical properties in drug design |
| title_full_unstemmed | A graph-based computational approach for modeling physicochemical properties in drug design |
| title_short | A graph-based computational approach for modeling physicochemical properties in drug design |
| title_sort | graph based computational approach for modeling physicochemical properties in drug design |
| topic | Molecular graph QSPR analysis Antibiotic compounds Topological indices Regression modeling |
| url | https://doi.org/10.1038/s41598-025-06624-3 |
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