QSAR modeling of antiradical properties of phenolic compounds using DFT calculations
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | QSAR modeling of antiradical properties of phenolic compounds using DFT calculations |
| 2. | Creator | Author's name, affiliation, country | H. Lafridi; Faculté Polydisciplinaire d'Ouarzazate Université Ibn Zohr |
| 2. | Creator | Author's name, affiliation, country | A. Oussa; Molecular Chemistry and Natural Substances Laboratory, University Moulay Ismai, Meknes. Morocco. |
| 2. | Creator | Author's name, affiliation, country | h. zgou |
| 2. | Creator | Author's name, affiliation, country | M. Bouachrine; Materials, Environment & Modeling Laboratory, Moulay Ismail University. |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Flavonoid derivatives; 2D-QSAR; DFT Study; Artificial neural network, PCA, MLR, MNLR, Domain of Applicability. |
| 4. | Description | Abstract | This paper describes a quantitative structure – activity relationship study of the antiradical properties of 31 flavonoids belonging to different groups such as: flavonols, flavonones, dihydroflavonols and biflavonones. Using density functional theory (DFT) calculations, some structural characteristics such as frontier molecular orbitals, molecular descriptors, have been studied. To gain insights into the chemical structure and property of the studied compounds, many types of descriptors are generated by using DFT/B3LYP 6-31G(d,p) and other software. Also, The Principle Component Analysis (PCA), Multiple Linear and Nonlinear Regression (MLR and MNLR), and Artificial Neural Network (ANN) have been investigated to select the descriptors, and to generate the correlation models that relate the structural feature to the biological activity. The statistical results of the MLR, MNLR, and ANN indicate that the determination coefficient R2 were 0.811, 0.646, 0.982, respectively. A good correlation coefficient is obtained, and the antiradical activities of these compounds are well predicted. These models are expected to be useful for screening of polyphenolic antioxidants. |
| 5. | Publisher | Organizing agency, location | |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 02-07-2020 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://revues.imist.ma/index.php/morjchem/article/view/20579 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.48317/IMIST.PRSM/morjchem-v8i4.20579 |
| 11. | Source | Title; vol., no. (year) | Moroccan Journal of Chemistry; Vol 8, No 4 (2020) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2020 Moroccan Journal of Chemistry |