Evaluation of a new series of pyrazole derivatives as a potent epidermal growth factor receptor inhibitory activity : QSAR modeling using quantum-chemical descriptors
© 2021 Wiley Periodicals LLC.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 42(2021), 32 vom: 15. Dez., Seite 2306-2320 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article DFT IC50 QSAR machine learning pyrazole derivatives Pyrazoles EGFR protein, human EC 2.7.10.1 ErbB Receptors |
| Zusammenfassung: | © 2021 Wiley Periodicals LLC. Pyrazole derivatives correspond to a family of heterocycle molecules with important pharmacological and physiological applications. At present, we perform a density functional theory (DFT) calculations and a quantitative structure-activity relationship (QSAR) evaluation on a series of 1-(4,5-dihydro-1H-pyrazol-1-yl) ethan-1-one and 4,5-dihydro-1H-pyrazole-1-carbothioamide derivatives as an epidermal growth factor receptor (EGFR) inhibitory activity. We thus propose a virtual screening protocol based on a machine-learning study. This theoretical model relates the studied compounds' biological activity to their calculated physicochemical descriptors. Moreover, the linear regression function is used to validate the model via the evaluation of Q2ext and Q2cv parameters for external and internal validations, respectively. Our QSAR model shows a good correlation between observed activities IC50 and predicted ones. Our model allows us to mitigate time-consuming problems and waste chemical and biological products in the preclinical phases |
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| Beschreibung: | Date Completed 21.02.2022 Date Revised 21.02.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.26761 |