Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study
Author(s) -
El Ghalia Hadaji,
Mohamed Bourass,
Abdelkrim Ouammou,
Mohammed Bouachrıne
Publication year - 2017
Publication title -
advances in physical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.269
H-Index - 17
eISSN - 1687-7993
pISSN - 1687-7985
DOI - 10.1155/2017/7629056
Subject(s) - quantitative structure–activity relationship , loo , aryl , sulfonamide , chemistry , linear regression , molecular descriptor , correlation coefficient , stereochemistry , computer science , organic chemistry , machine learning , alkyl
(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model
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