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Quantitative Structure–Activity Relationship (QSAR) Studies of Some Glutamine Analogues for Possible Anticancer Activity


 
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1. Title Title of document Quantitative Structure–Activity Relationship (QSAR) Studies of Some Glutamine Analogues for Possible Anticancer Activity
 
2. Creator Author's name, affiliation, country B. Elidrissi, A. Ousaa, M. Ghamali, S. Chtita, M. A. Ajana, M. Bouachrine, T. Lakhlifi; Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes B. Elidrissi, A. Ousaa, M. Ghamali, S. Chtita, M. A. Ajana, M. Bouachrine, T. Lakhlifi; Morocco
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) DFT, QSAR, tumor cells, Artificial Neural Network, Cross Validation
 
4. Description Abstract

A Quantitative Structure–Activity Relationship (QSAR) study was performed to predict an anticancer activity in tumor cells of thirty-six 5-N-substituted-2-(substituted benzenesulphonyl) glutamines compounds using the electronic and topologic descriptors computed respectively, with ACD/ChemSketch and Gaussian 03W programs. The structures of all 36 compounds were optimized using the hybrid Density Functional Theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 30 compounds were assigned as the training set and the rest as the test set. These compounds were analyzed by the Principal Components Analysis (PCA) method, a descendant Multiple Linear Regression (MLR), Multiple Nonlinear Regression (MNLR) analyses and an Artificial Neural Network (ANN). The robustness of the obtained models was assessed by leave-many-out cross-validation, and external validation through a test set.

This study shows that the ANN has served marginally better to predict antitumor activity when compared with the results given by predictions made with MLR and MNLR
 
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7. Date (YYYY-MM-DD) 30-07-2018
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://revues.imist.ma/index.php/morjchem/article/view/9333
 
10. Identifier Digital Object Identifier (DOI) https://doi.org/10.48317/IMIST.PRSM/morjchem-v6i4.9333
 
11. Source Title; vol., no. (year) Moroccan Journal of Chemistry; Vol 6, No 4 (2018)
 
12. Language English=en en
 
13. Relation Supp. Files
 
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15. Rights Copyright and permissions Copyright (c) 2018 Moroccan Journal of Chemistry