A paradigmatic approach to the topological measure of babesiosis drugs and estimating physical properties via QSPR analysis
New developments in the field of chemical graph theory have made it easier to comprehend how chemical structures relate to the graphs that underlie them on a more profound level using the ideas of classical graph theory. Chemical graphs can be effectively probed with the help of quantitative structu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024176463 |
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| Summary: | New developments in the field of chemical graph theory have made it easier to comprehend how chemical structures relate to the graphs that underlie them on a more profound level using the ideas of classical graph theory. Chemical graphs can be effectively probed with the help of quantitative structure-property relationship (QSPR) analysis. In order to statistically correlate physical attributes. Earlier research on medications motivated us to explore the quantitative structure-property relationships (QSPR) of babesiosis. We examined the babesiosis drugs data and used topological indices to do this. A measurement that reflects the theoretical characteristics of drugs is a topological index. The chemical structures of drugs that relieve pain are studied using well-known degree-based topological indices. Mepron, azithromycin, clindamycin, imidocarb, triclosan and other medications are among them. These drugs are administered to minimize or eliminate the discomfort that is felt in the affected location. The chemical structure of a molecule is represented as a graph. Further research into topological indices' QSPR analysis revealed so as a substantial link with the physical characteristics of drugs utilized in the production of drugs to halt the disease. The analysis of topological indices served as evidence for this. The results demonstrate how well the QSPR experiments on applying regression technique are useful in development of novel drugs for babesiosis and predict the characteristics of babesiosis drugs. The usefulness of applying TIs in this situation is demonstrated by the linear regression model's minimum error and correct prediction of these attributes. |
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| ISSN: | 2405-8440 |