Literature DB >> 11302584

Neural networks studies: quantitative structure-activity relationships of antifungal 1.

S Mghazli1, A Jaouad, M Mansour, D Villemin, D Cherqaoui.   

Abstract

Models of relationships between structure and antifungal activity of 1-[2-(substituted phenyl)allyl]imidazoles and related compounds were constructed by means of a multilayer neural network using the back-propagation (BP) algorithm. Each molecule was described by three structural and one physicochemical parameters. The leave-one-out procedure was used to assess the predictive ability of a neural network model. The results obtained were compared to those given in the literature by the multiple linear regression (MLR), and were found to be better. The contribution of each descriptor to the structure-activity relationships was evaluated. Hydrophobicity of the molecule was confirmed to take the most relevant part in the molecular description.

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Year:  2001        PMID: 11302584     DOI: 10.1016/s0045-6535(00)00111-9

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  3 in total

1.  QSAR of heterocyclic antifungal agents by flip regression.

Authors:  Omar Deeb; Brian W Clare
Journal:  J Comput Aided Mol Des       Date:  2008-06-24       Impact factor: 3.686

2.  Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks.

Authors:  Julio Caballero; Michael Fernández
Journal:  J Mol Model       Date:  2005-10-21       Impact factor: 1.810

3.  Artificial neural networks: non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors.

Authors:  Latifa Douali; Didier Villemin; Abdelmajid Zyad; Driss Cherqaoui
Journal:  Mol Divers       Date:  2004       Impact factor: 2.943

  3 in total

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