Phenol derivatives are well known by their toxicological effect on environment and human health. However, like most chemicals, the experimental determination of phenols toxicity is a very demanding and complex task. Hence, there is a great motivation for the development of quantitative structure-cytotoxicity relationship (QSCR) that can predict accurately the cytotoxicity of new phenolic compounds. Machine learning (ML) provides a set of methods that exhibit high prediction accuracy and outperforms many other approaches. In the current work, deep neural networks techniques were used to perform QSCR for a set of substituted phenol compounds. Both electron-releasing substituents and electron-withdrawing substituents were considered showing the impact of the electronic effect on the model establishment. The model established was highly successful in predicting cytotoxicity of new compounds.


Phenols Cytotoxicity Machine learning Deep neural networks Predictive ability 


  1. 1.
    Vermerris, W., Nicholson, R.: Phenolic Compound Biochemistry. Springer, Netherlands (2006)Google Scholar
  2. 2.
    Shvedova, A.A., et al.: Redox cycling of phenol induces oxidative stress in human epidermal keratinocytes. J. Invest. Dermatol. 114(2), 354–364 (2000)CrossRefGoogle Scholar
  3. 3.
    Shalaby, S., Horwitz, B.A.: Plant phenolic compounds and oxidative stress: integrated signals in fungal–plant interactions. Curr. Genet. 61(3), 347–357 (2015)CrossRefGoogle Scholar
  4. 4.
    Villegas, L.G.C., Mashhadi, N., Chen, M., Mukherjee, D., Taylor, K.E., Biswas, N.: A short review of techniques for phenol removal from wastewater. Curr. Pollut. Rep. 2(3), 157–167 (2016)CrossRefGoogle Scholar
  5. 5.
    Yu, M.-H., Yang, T.-Y., Ho, H.-H., Huang, H.-P., Chan, K.-C., Wang, C.-J.: Mulberry polyphenol extract inhibits FAK/Src/PI3 K complex and related signaling To regulate the migration in A7r5 cells. J. Agric. Food Chem. 66(15), 3860–3869 (2018)CrossRefGoogle Scholar
  6. 6.
    Gramec Skledar, D., Peterlin Mašič, L.: Bisphenol A and its analogs: Do their metabolites have endocrine activity? Environ. Toxicol. Pharmacol. 47, 182–199 (Oct 2016)CrossRefGoogle Scholar
  7. 7.
    Yehye, W.A., et al.: Understanding the chemistry behind the antioxidant activities of butylated hydroxytoluene (BHT): a review. Eur. J. Med. Chem. 101, 295–312 (2015)CrossRefGoogle Scholar
  8. 8.
    Murray, A.R., Kisin, E., Castranova, V., Kommineni, C., Gunther, M.R., Shvedova, A.A.: Phenol-induced in vivo oxidative stress in skin: evidence for enhanced free radical generation, thiol oxidation, and antioxidant depletion. Chem. Res. Toxicol. 20(12), 1769–1777 (2007)CrossRefGoogle Scholar
  9. 9.
    Douali, L., Villemin, D., Cherqaoui, D.: Comparative QSAR based on neural networks for the anti-HIV activity of HEPT derivatives. Curr. Pharm. Des. 9(22), 1817–1826 (2003)CrossRefGoogle Scholar
  10. 10.
    Cherkasov, A., et al.: QSAR modeling: where have you been? where are you going to? J. Med. Chem. 57(12), 4977–5010 (2014)CrossRefGoogle Scholar
  11. 11.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)Google Scholar
  13. 13.
    Goh, G.B., Hodas, N.O., Vishnu, A.: Deep learning for computational chemistry. J. Comput. Chem. 38(16), 1291–1307 (2017)CrossRefGoogle Scholar
  14. 14.
    Yu, D., Deng, L.: Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process. Mag. 28(1), 145–154 (2011)CrossRefGoogle Scholar
  15. 15.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)Google Scholar
  16. 16.
    Loussaief, S., Abdelkrim, A.: Machine learning framework for image classification. In: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 58–61 (2016)Google Scholar
  17. 17.
    Chabchoub, S., Mansouri, S., Ben Salah, R.: Impedance Cardiography heartbeat classification using LP, DWT, KNN and SVM. In: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 53–57 (2016)Google Scholar
  18. 18.
    Mabrouk, H.H.: Machine learning from experience feedback on accidents in transport. In: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 246–251 (2016)Google Scholar
  19. 19.
    Selassie, C.D., et al.: Comparative QSAR and the radical toxicity of various functional groups. Chem. Rev. 102(7), 2585–2605 (2002)CrossRefGoogle Scholar
  20. 20.
    Selassie, C.D., Kapur, S., Verma, R.P., Rosario, M.: Cellular apoptosis and cytotoxicity of phenolic compounds: a quantitative structure-activity relationship study. J. Med. Chem. 48(23), 7234–7242 (2005)CrossRefGoogle Scholar
  21. 21.
    Garg, R., Kurup, A., Hansch, C.: Comparative QSAR: on the toxicology of the phenolic OH moiety. Crit. Rev. Toxicol. 31(2), 223–245 (2001)CrossRefGoogle Scholar
  22. 22.
    Moridani, M.Y., Siraki, A., O’Brien, P.J.: Quantitative structure toxicity relationships for phenols in isolated rat hepatocytes. Chem. Biol. Interact. 145(2), 213–223 (2003)CrossRefGoogle Scholar
  23. 23.
    Dieguez-Santana, K., Pham-The, H., Villegas-Aguilar, P.J., Le-Thi-Thu, H., Castillo-Garit, J.A., Casañola-Martin, G.M.: Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database. Chemosphere 165, 434–441 (2016)CrossRefGoogle Scholar
  24. 24.
    Aptula, A.O., Roberts, D.W., Cronin, M.T.D., Schultz, T.W.: Chemistry-toxicity relationships for the effects of di- and trihydroxybenzenes to Tetrahymena pyriformis. Chem. Res. Toxicol. 18(5), 844–854 (2005)CrossRefGoogle Scholar
  25. 25.
    Xu, L., Ball, J.W., Dixon, S.L., Jurs, P.C.: Quantitative structure-activity relationships for toxicity of phenols using regression analysis and computational neural networks. Environ. Toxicol. Chem. 13(5), 841–851 (1994)CrossRefGoogle Scholar
  26. 26.
    Selassie, C.D., DeSoyza, T.V., Rosario, M., Gao, H., Hansch, C.: Phenol toxicity in leukemia cells: a radical process? Chem. Biol. Interact. 113(3), 175–190 (1998)CrossRefGoogle Scholar
  27. 27.
    Selassie, C., Verma, R.P.: QSAR of toxicology of substituted phenols. J. Pestic. Sci. 40(1), 1–12 (2015)CrossRefGoogle Scholar
  28. 28.
    Hansch, C., Zhang, L.: Comparative QSAR: Radical toxicity and scavenging. Two different sides of the same coin. SAR QSAR Environ. Res. 4(2–3), 73–82 (1995)CrossRefGoogle Scholar
  29. 29.
    Hansch, C., McKarns, S.C., Smith, C.J., Doolittle, D.J.: Comparative QSAR evidence for a free-radical mechanism of phenol-induced toxicity. Chem. Biol. Interact. 127(1), 61–72 (2000)CrossRefGoogle Scholar
  30. 30.
    Cronin, M.T.D., et al.: Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis. Chemosphere 49(10), 1201–1221 (2002)CrossRefGoogle Scholar
  31. 31.
    Chollet, F.: Keras (2015)Google Scholar
  32. 32.
    Abadi, M. et al.: TensorFlow: a system for large-scale machine learning. Google AI [Online]. Available: (2016). Accessed 15 Sep 2018
  33. 33.
    Douali, L., Villemin, D., Zyad, A., Cherqaoui, D.: Artificial neural networks: non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors. Mol. Divers. 8(1), 1–8 (2004)CrossRefGoogle Scholar
  34. 34.
    Douali, L., Villemin, D., Cherqaoui, D., Douali, L., Villemin, D., Cherqaoui, D.: Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by neural networks: TIBO derivatives. Int. J. Mol. Sci. 5(2), 48–55 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Regional Center for Training and Education Marrakech-SafiMarrakechMorocco

Personalised recommendations