On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling
Abstract In this paper, we discussed the role of topological descriptors in the QSPR modeling of pulmonary cancer drugs. Degree-based topological indices were computed using computational methods driven by Python that are mathematical representations of properties of molecules without physical measu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88419-0 |
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| author | Huiling Qin Mazhar Hussain Muhammad Farhan Hanif Muhammad Kamran Siddiqui Zahid Hussain Mohamed Abubakar Fiidow |
| author_facet | Huiling Qin Mazhar Hussain Muhammad Farhan Hanif Muhammad Kamran Siddiqui Zahid Hussain Mohamed Abubakar Fiidow |
| author_sort | Huiling Qin |
| collection | DOAJ |
| description | Abstract In this paper, we discussed the role of topological descriptors in the QSPR modeling of pulmonary cancer drugs. Degree-based topological indices were computed using computational methods driven by Python that are mathematical representations of properties of molecules without physical measurement. These descriptors were analyzed through linear regression models using SPSS software to predict significant physicochemical properties like boiling point, flash point, molar refractivity, and polarizability. The results show excellent correlations between the computed indices and the observed properties, except for flash point, which ascertains the dependability of the approach in QSPR analysis. The integration of computational and mathematical chemistry will make it easier to evaluate drugs because it can assure consistent data for preclinical development. The paper also reveals specific indices that are superior to others regarding predictive accuracy, thus giving a basis for refining the models to suit the individual compound. This review sets the pace for establishing methodologies that are efficient in designing new and efficient treatments against cancer since it gives insight into the strengths and limitations of topological modeling. This work marked the transformation in accelerating the math involved in drug discovery to reduce such research costs. |
| format | Article |
| id | doaj-art-0cf9c80b6bbb4c60b3ac27954fc68f42 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0cf9c80b6bbb4c60b3ac27954fc68f422025-02-02T12:20:13ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-88419-0On QSPR analysis of pulmonary cancer drugs using python-driven topological modelingHuiling Qin0Mazhar Hussain1Muhammad Farhan Hanif2Muhammad Kamran Siddiqui3Zahid Hussain4Mohamed Abubakar Fiidow5Department of Rehabilitation Medicine, The Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Mathematics, COMSATS University IslamabadDepartment of Mathematics and Statistics, The University of LahoreDepartment of Mathematics, COMSATS University IslamabadDepartment of Mathematics and Statistics, The University of LahoreDepartment of Mathematical Sciences, Faculty of Science, Somali National UniversityAbstract In this paper, we discussed the role of topological descriptors in the QSPR modeling of pulmonary cancer drugs. Degree-based topological indices were computed using computational methods driven by Python that are mathematical representations of properties of molecules without physical measurement. These descriptors were analyzed through linear regression models using SPSS software to predict significant physicochemical properties like boiling point, flash point, molar refractivity, and polarizability. The results show excellent correlations between the computed indices and the observed properties, except for flash point, which ascertains the dependability of the approach in QSPR analysis. The integration of computational and mathematical chemistry will make it easier to evaluate drugs because it can assure consistent data for preclinical development. The paper also reveals specific indices that are superior to others regarding predictive accuracy, thus giving a basis for refining the models to suit the individual compound. This review sets the pace for establishing methodologies that are efficient in designing new and efficient treatments against cancer since it gives insight into the strengths and limitations of topological modeling. This work marked the transformation in accelerating the math involved in drug discovery to reduce such research costs.https://doi.org/10.1038/s41598-025-88419-0Pulmonary cancer drugsNetworksLinear regression modelTopological descriptorPython technique |
| spellingShingle | Huiling Qin Mazhar Hussain Muhammad Farhan Hanif Muhammad Kamran Siddiqui Zahid Hussain Mohamed Abubakar Fiidow On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling Scientific Reports Pulmonary cancer drugs Networks Linear regression model Topological descriptor Python technique |
| title | On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling |
| title_full | On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling |
| title_fullStr | On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling |
| title_full_unstemmed | On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling |
| title_short | On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling |
| title_sort | on qspr analysis of pulmonary cancer drugs using python driven topological modeling |
| topic | Pulmonary cancer drugs Networks Linear regression model Topological descriptor Python technique |
| url | https://doi.org/10.1038/s41598-025-88419-0 |
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