Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis
The study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the re...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1348407/full |
| _version_ | 1797316810275553280 |
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| author | Ali Raza Muhammad Mobeen Munir |
| author_facet | Ali Raza Muhammad Mobeen Munir |
| author_sort | Ali Raza |
| collection | DOAJ |
| description | The study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the research aimed to uncover the correlations between the chemical structures of these medications and their unique properties. The results illuminated the quantitative relationships between structural characteristics and pharmacological traits, advancing our predictive capabilities. This research significantly contributes to medication discovery and design by providing essential insights into the structure-property connections of these medicinal compounds. Notably, certain spectrum-based descriptors, such as positive inertia energy, adjacency energy, arithmetic-geometric energy, first zegrab energy, and the harmonic index, exhibited strong correlation coefficients above 0.999. In contrast, well-known descriptors like the Extended adjacency, Laplacian and signless Laplacian spectral radii, and the first and second Zagreb Estrada indices showed weaker performance. The article emphasizes the application of graph energies and a linear regression model to predict pharmacological features effectively, enhancing the drug discovery process and aiding in targeted drug design by elucidating the relationship between molecular structure and pharmacological characteristics. |
| first_indexed | 2024-03-08T03:24:44Z |
| format | Article |
| id | doaj.art-b212cb061f34481fa3bcf80c0edcd1b8 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-03-08T03:24:44Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-b212cb061f34481fa3bcf80c0edcd1b82024-02-12T04:15:18ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-02-011210.3389/fphy.2024.13484071348407Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysisAli RazaMuhammad Mobeen MunirThe study centered on Quantitative Structure Property Relationship (QSPR) analysis with a focus on various graph energies, investigating drugs like Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Employing computational modeling techniques, the research aimed to uncover the correlations between the chemical structures of these medications and their unique properties. The results illuminated the quantitative relationships between structural characteristics and pharmacological traits, advancing our predictive capabilities. This research significantly contributes to medication discovery and design by providing essential insights into the structure-property connections of these medicinal compounds. Notably, certain spectrum-based descriptors, such as positive inertia energy, adjacency energy, arithmetic-geometric energy, first zegrab energy, and the harmonic index, exhibited strong correlation coefficients above 0.999. In contrast, well-known descriptors like the Extended adjacency, Laplacian and signless Laplacian spectral radii, and the first and second Zagreb Estrada indices showed weaker performance. The article emphasizes the application of graph energies and a linear regression model to predict pharmacological features effectively, enhancing the drug discovery process and aiding in targeted drug design by elucidating the relationship between molecular structure and pharmacological characteristics.https://www.frontiersin.org/articles/10.3389/fphy.2024.1348407/fullregression modelgraph spectrumspectral radiuspharmacological traitscorrelation coefficient |
| spellingShingle | Ali Raza Muhammad Mobeen Munir Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis Frontiers in Physics regression model graph spectrum spectral radius pharmacological traits correlation coefficient |
| title | Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis |
| title_full | Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis |
| title_fullStr | Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis |
| title_full_unstemmed | Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis |
| title_short | Exploring spectrum-based descriptors in pharmacological traits through quantitative structure property (QSPR) analysis |
| title_sort | exploring spectrum based descriptors in pharmacological traits through quantitative structure property qspr analysis |
| topic | regression model graph spectrum spectral radius pharmacological traits correlation coefficient |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1348407/full |
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