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Prediction of tyrosinase inhibition for drug design using the genetic algorithm–multiple linear regressions

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Abstract

Modeling inhibition data for a series of most potent tyrosinase inhibitors for hyper-pigmentation treatment was done with quantitative structure–activity relationship (QSAR). In this report, multiple linear regression (MLR) methodology coupled with feature selection method, genetic algorithm (GA), was applied to derive QSAR models. A model with seven selected descriptors was obtained by the database that consisted of 49 compounds. The power of the model for prediction was verified with the leave-one-out and leave-group-out cross-validation test, which have values of 0.766 and 0.795, respectively. Moreover, the statistical parameters provided by the GA–MLR model (\( R_{\text{train}}^{ 2} \) = 0.85, \( R_{\text{test}}^{ 2} \) = 0.84, and \( F_{\text{train}}^{ 2} \) = 24.498) lead to a better understanding of the structural requirements of designing novel potent tyrosinase inhibitors. The study showed that the number of six-membered rings, the branching of a molecule, the van der Waals volume of atoms, the lopping centric index, the global charge transfer in a molecule and atomic masses are the main criteria in the inhibitory activity of compounds.

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Acknowledgments

The support of the Research Councils of the University of Tehran, and Tehran University of Medical Sciences are gratefully acknowledged.

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Authors and Affiliations

  1. Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P.O. Box 14155-6455, Tehran, Iran

    Roya Bazl, Mohammad Reza Ganjali & Parviz Norouzi

  2. Institute of Biochemistry and Biophysics, University of Tehran, P.O. Box 14115-111, Tehran, Iran

    Hossein Derakhshankhah & Ali Akbar Saboury

  3. Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, P.O. Box 14155-6451, Tehran, Iran

    Massoud Amanlou

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  1. Roya Bazl
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  2. Mohammad Reza Ganjali
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  3. Hossein Derakhshankhah
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  4. Ali Akbar Saboury
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  5. Massoud Amanlou
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  6. Parviz Norouzi
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Correspondence to Mohammad Reza Ganjali.

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Bazl, R., Ganjali, M.R., Derakhshankhah, H. et al. Prediction of tyrosinase inhibition for drug design using the genetic algorithm–multiple linear regressions. Med Chem Res 22, 5453–5465 (2013). https://doi.org/10.1007/s00044-012-0440-0

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  • DOI: https://doi.org/10.1007/s00044-012-0440-0

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