Hmammouchi, R. and Taghki, A.I. and Larif, M. and Adad, A. and Abdellaoui, A. and Bouachrine, M. and Lakhlifi, T. (2013) Combining DFT and QSAR result for predicting the biological activity of the phenylsuccinimide derivatives. Journal of Chemical and Pharmaceutical Research, 5 (10). pp. 45-56.

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Abstract

Study 3D-QSAR is applied to a set of 57 molecules based on N-phenylsuccinimides using the principal component analysis (PCA) method, the multiple linear regression method (MLR) and the artificial neural network (ANN). The predicted values of activities are in good agreement with the experimental results. The artificial neural network (ANN) techniques, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0,9 with an 8-20-1 ANN model which is a good result. As a result of quantitative structure-activity relationships, we found that the model proposed in this study is constituted of major descriptors used to describe these molecules. The obtained results suggested that proposed combination of several calculated parameters could be useful in predicting biological activity of N-phenylsuccinimides derivatives.

Item Type: Article
Uncontrolled Keywords: 3D-QSAR; ANN; DFT study; MLR; PCA, Computational chemistry; Linear regression; Molecular graphics; Molecules; Neural networks; Principal component analysis; Three dimensional, Bioactivity, antifungal agent; phenylsuccinimide derivative; succinimide derivative; unclassified drug, article; artificial neural network; biological activity; controlled study; correlation coefficient; density functional theory; multiple linear regression analysis; prediction; principal component analysis; quantitative structure activity relation
Subjects: Pharmacology, Toxicology and Pharmaceutics
Divisions: SCIENTIFIC PRODUCTION > Pharmacology, Toxicology and Pharmaceutics
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:49
URI: http://eprints.umi.ac.ma/id/eprint/4399

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