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Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations

Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations

B. Elidrissi, A. Ousaa, M. Ghamali, S. Chtita, M. A. Ajana, M. Bouachrine, T. Lakhlifi
Copyright: © 2018 |Volume: 3 |Issue: 1 |Pages: 15
ISSN: 2379-7487|EISSN: 2379-7479|EISBN13: 9781522547235|DOI: 10.4018/IJQSPR.2018010107
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MLA

Elidrissi, B., et al. "Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations." IJQSPR vol.3, no.1 2018: pp.119-133. http://doi.org/10.4018/IJQSPR.2018010107

APA

Elidrissi, B., Ousaa, A., Ghamali, M., Chtita, S., Ajana, M. A., Bouachrine, M., & Lakhlifi, T. (2018). Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 3(1), 119-133. http://doi.org/10.4018/IJQSPR.2018010107

Chicago

Elidrissi, B., et al. "Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations," International Journal of Quantitative Structure-Property Relationships (IJQSPR) 3, no.1: 119-133. http://doi.org/10.4018/IJQSPR.2018010107

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Abstract

A Quantitative Structure–Activity Relationship (QSAR) study was performed to predict HIV-1 integrase inhibition activity (pIC50) of thirty-five 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4-carboxamide compounds using the electronic and physico-chemical descriptors computed respectively, with Gaussian 03W and ACD/ChemSketch programs. The structures of all compounds were optimized using the hybrid Density Functional Theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 28 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, the descendant Multiple Linear Regression (MLR) analyses and the 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 MLR has served marginally better to predict pIC50 activity, when compared with the results given by predictions made with a (4-3-1) ANN model.

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