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

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Abstract

Structure-anti HIV activity relationships were established for a sample of 801-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine(HEPT) using a three-layer neural network (NN). Eight structural descriptors and physicochemical variables were used to characterize the HEPT derivatives under study. The network's architecture and parameters were optimized in order to obtain good results. All the NN architectures were able to establish a satisfactory relationship between the molecular descriptors and the anti-HIV activity. NN proved to give better results than other models in the literature. NN have been shown to be particularly successful in their ability to identify non-linear relationships.

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Authors and Affiliations

  1. Département de Chimie, Faculté des Sciences Semlalia, Université Cadi Ayyad, BP 2390, Marrakech, Morocco

    Latifa Douali & Driss Cherqaoui

  2. Ecole Nationale Supérieure d'Ingénieurs (E.N.S.I.), I. S. M. R. A., LCMT, UMR CNRS no. 6507, 6 boulevard Maréchal Juin, 14050, caen Cedex, France

    Didier Villemin

  3. Laboratoire d'Immunologie, Biochimie et Biologie Moléculaire, Faculté des Sciences et Techniques, Université Cadi-Ayyad, B.P. 523, Béni-Mellal, Morocco

    Abdelmajid Zyad

Authors
  1. Latifa Douali
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  2. Didier Villemin
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  3. Abdelmajid Zyad
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  4. Driss Cherqaoui
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Correspondence to Driss Cherqaoui.

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Douali, L., Villemin, D., Zyad, A. et al. Artificial neural networks: Non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors. Mol Divers 8, 1–8 (2004). https://doi.org/10.1023/B:MODI.0000006753.11500.37

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  • DOI: https://doi.org/10.1023/B:MODI.0000006753.11500.37

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