alexa QSPR and DFT Studies on the Melting Point of Carbocyclic Nitroaromatic Compounds
ISSN: 2161-0398

Journal of Physical Chemistry & Biophysics
Open Access

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Research Article

QSPR and DFT Studies on the Melting Point of Carbocyclic Nitroaromatic Compounds

Elidrissi B1*, Ousaa A1, Ghamali M1, Chtita S1, Ajana MA1, Bouachrine M2 and Lakhlifi T1

1Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes, Morocco

2ESTM, University Moulay Ismail, Meknes, Morocco

*Corresponding Author:
Elidrissi B
Molecular Chemistry and Natural
Substances Laboratory, Faculty of Science
University Moulay Ismail, Meknes, Morocco
Tel: +212607662438
E-mail: [email protected]

Received Date: May 24, 2017 Accepted Date: May 26, 2017 Published Date: June 06, 2017

Citation: Elidrissi B, Ousaa A, Ghamali M, Chtita S, Ajana MA, et al. (2017) QSPR and DFT Studies on the Melting Point of Carbocyclic Nitroaromatic Compounds. J Phys Chem Biophys 7: 245. doi: 10.4172/2161-0398.1000245

Copyright: © 2017 Elidrissi B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

A quantitative structure-property relationship (QSPR) study was performed to predict the melting points of 60 carbocyclic nitroaromatic compounds using the electronic and topologic descriptors computed respectively, with ACD/ ChemSketch and Gaussian 03W programs. The structures of all 60 compounds were optimized using the hybrid density functional theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 50 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) 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 test set. This study shows that the PCA and MLR have served also to predict melting point and some other physicochemical properties, but when compared with the results given by the ANN (R=0.997), we realized that the predictions fulfilled by this latter were more effective and much better than other models.

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