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Quantitative structure–activity relationships study of tyrosinase inhibitors using logistic regression and artificial neural networks

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Abstract

In this work, two logistic regression (LR) and artificial neural networks (ANNs) were applied for seeking quantitative structure–activity relationships (QSARs) that correlate structural descriptors and inhibition activity of tyrosinase inhibitors. This study was devoted to extraction of the most effective structural features of these inhibitors from a large number of quantitative descriptors. Many quantitative descriptors were generated to express the physicochemical properties of 35 compounds with optimized structures. After filtration of these descriptors, 39 of them remained and selected for QSAR study. Logistic regression was used to non-linearly select different subsets of descriptors and develop for prediction of IC50. The best subset then fed to artificial neural network as non-linear procedure. The best prediction model was found to be a 3–11–1 artificial neural network. The evaluating indices (FC, FAR and POD) of this prediction model were found 84.75, 13.48 and 93.15 %, respectively. The conformability of results by the network model and logistic regression ensured the effectiveness of the selected structural features (descriptors).

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Authors and Affiliations

  1. Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran

    M. A. Mahmood Janlou & A. Eidi

  2. Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box 14115/175, Tehran, Iran

    P. Abdolmaleki

  3. Department of Chemistry, Mazandaran University, Babolsar, Iran

    M. Tajbakhsh

  4. Department of Medicinal Chemistry, Drug Design and Development Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran

    M. Amanlou

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  1. M. A. Mahmood Janlou
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  2. P. Abdolmaleki
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  3. M. Tajbakhsh
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  4. M. Amanlou
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  5. A. Eidi
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Correspondence to M. A. Mahmood Janlou.

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Mahmood Janlou, M.A., Abdolmaleki, P., Tajbakhsh, M. et al. Quantitative structure–activity relationships study of tyrosinase inhibitors using logistic regression and artificial neural networks. J IRAN CHEM SOC 9, 643–653 (2012). https://doi.org/10.1007/s13738-012-0083-6

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  • DOI: https://doi.org/10.1007/s13738-012-0083-6

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