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Int. J. Mol. Sci. 2003, 4(5), 249-262; doi:10.3390/i4050249
Article

Quantitative Structure-Activity Relationships of Noncompetitive Antagonists of the NMDA Receptor: A Study of a Series of MK801 Derivative Molecules Using Statistical Methods and Neural Network

1,* , 1, 1, 2 and 3
1 Département de chimie Faculté des Sciences et Techniques Fès-Saïss Université Sidi Mohamed Ben Abdellah B.P.2202 Route D’Immouzzer, Fès, Morocco 2 Département de Physique, Faculté des Sciences et Techniques Fès-Saïss Université Sidi Mohamed Ben Abdellah B.P.2202 Route D’Immouzzer, Fès, Morocco 3 Département de chimie Faculté des sciences Université Moulay Ismail B.P.4010 Bni M’Hamed Meknes, Morocco
* Author to whom correspondence should be addressed.
Received: 12 December 2001 / Accepted: 8 October 2002 / Published: 15 April 2003
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Abstract

From a series of 50 MK801 derivative molecules, a selected set of 44 compounds was submitted to a principal components analysis (PCA), a multiple regression analysis (MRA), and a neural network (NN). This study shows that the compounds' activity correlates reasonably well with the selected descriptors encoding the chemical structures. The correlation coefficients calculated by MRA and there after by NN, r = 0.986 and r = 0.974 respectively, are fairly good to evaluate a quantitative model, and to predict activity for MK801 derivatives. To test the performance of this model, the activities of the remained set of 6 compounds are deduced from the proposed quantitative model, by NN. This study proved that the predictive power of this model is relevant.
Keywords: structure-activity relationships; noncompetitive antagonists; MK801 derivatives; NMDA receptor; principal components analysis (PCA); multiple regression analysis (MRA); neural network (NN) structure-activity relationships; noncompetitive antagonists; MK801 derivatives; NMDA receptor; principal components analysis (PCA); multiple regression analysis (MRA); neural network (NN)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Elhallaoui, M.; Elasri, M.; Ouazzani, F.; Mechaqrane, A.; Lakhlifi, T. Quantitative Structure-Activity Relationships of Noncompetitive Antagonists of the NMDA Receptor: A Study of a Series of MK801 Derivative Molecules Using Statistical Methods and Neural Network. Int. J. Mol. Sci. 2003, 4, 249-262.

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Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert