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- MakaleYalnız künye2024Scopus SJRQ3Artificial Neural Network and Multiple RegresAnalysis Applied to 2D-QSAR Studies: the Caof Imidazolidine-2,4-dione as PTP1B Inhibito
Aggoun, Siham; Belaidi, Salah; Ghamri, Meriam; Kerassa, Aicha; Cinar, Mehmet; Yamari, Imane; Abchir, Oussama; Chtita, Samir
In this investigation, we undertook a comprehensive quantitative structure-activity relationship (QSAR) analysis using both modern artificial neural network (ANN) methods and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine-2,4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the ANN architecture (6-4-1) over the MLR method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study’s significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics.
AtıfScopus2
İncele - MakaleAçık Erişim2018Scopus SJRQ3Qualitative and quantitative structure-activity relationships studies of quercetin derivatives as chemotherapeutic activity
Rouane, Abderrahmane; Tchouar, Noureddine; Kerassa, Aicha; Cinar, Mehmet; Belaidi, Salah
Qualitative and Quantitative structure activity relationship (SAR/QSAR) analysis was applied to eighteen Quercetin derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the Quercetin derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models.
AtıfScopus3OpenAlex3İncele - MakaleYalnız künye2018Scopus SJRQ3Drug likeness scoring and quantitative structure anaplastic lymphoma kinase (ALK) inhibitors activities relationships of pyrazolone derivatives
Ouadah, Karim; Tchouar, Noureddine; Belaidi, Salah; Oukil, Oualid; Cinar, Mehmet
Series of twenty-four compounds was the field for applying multi-parameter optimization (MPO) methods and qualitative approximations of structural activity relationships in order to determine the role of various physico-chemical and quantum descriptors used in QSAR modeling as independent variables. Anaplastic lymphoma kinase (ALK) inhibitory activity was considered as a component in this study. In order to illustrate the quantitative relationships between the molecular descriptors and the activity of pyrazolone derivatives, we have applied a multiple linear regression (MLR) procedure. By adopting cross-validation with the leave-one-out method, we could estimate the predictability of models. Our results suggest a QSAR model based on the following descriptors: S, V, LogP, POL, Ref, HOMO, LUMO, E, , HBA, PSA and NRB, for the inhibitory activities against ALK. Results show high correlation between experimental and predicted activity values, indicating the validation and the good quality of the QSAR model.
AtıfOpenAlex1İncele