Advertisement

Bioactivity Prediction Using Convolutional Neural Network

  • Hentabli HamzaEmail author
  • Maged Nasser
  • Naomie Salim
  • Faisal Saeed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

According to the similar property principle, structurally similar compounds exhibit very similar properties as well as similar biological activities. Many researchers have applied this principle to discover novel drugs, thereby leading to the emergence of the prediction of the activities of compounds based on their chemical structure, since the toxic or biological properties of compounds are determined by their chemical structure, particularly, their substructures. The concept of functional groups (FGs) of connected atoms (small molecules) determining the properties and reactivity of the parent molecule forms the cornerstone of organic chemistry, medicinal chemistry, toxicity assessments and QSAR. This study introduced a novel predictive system, i.e., a convolutional neural network that enables the prediction of molecular bioactivities using a novel molecular matrix representation. The number of atoms in small molecules were investigated to determine its accuracy during the prediction of the activities of the orphan compounds. This approach was applied to popular datasets and the performance of this system was compared with three other classical ML algorithms. All the experiments indicated that the proposed model was able to provide an interesting prediction rate (accuracy of 90.21).

Keywords

Bioactive molecules Activity prediction model Convolutional neural network Deep learning Biological activities 

Notes

Acknowledgment

This work is supported by the Ministry of Higher Education (MOHE) and the Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM) under the Research University Grant Category (VOT Q.J130000.2528.16H74 and R.J130000.7828.4F985).

References

  1. 1.
    Ammar, A., Leclère, V., Jacques, P., Salim, N., Pupin, M.: Prediction of new bioactive molecules using a Bayesian belief network. J. Chem. Inf. Model. 54(1), 30–36 (2014)CrossRefGoogle Scholar
  2. 2.
    Barakat, K.: Computer-aided drug design. J. Pharm. Care Heal. Syst. 1(4), 1–2 (2014)Google Scholar
  3. 3.
    Kothiwale, S., Borza, C., Pozzi, A., Meiler, J.: Quantitative structure–activity relationship modeling of kinase selectivity profiles. Molecules 22(9), 1–11 (2017)CrossRefGoogle Scholar
  4. 4.
    Willett, P., Wilton, D., Hartzoulakis, B., Tang, R., Ford, J., Madge, D.: Prediction of ion channel activity using binary kernel discrimination. J. Chem. Inf. Model. 47(5), 1961–1966 (2007)CrossRefGoogle Scholar
  5. 5.
    Chen, B., Mueller, C., Willett, P.: Evaluation of a Bayesian inference network for ligand-based virtual screening. J. Cheminform. 1(1), 1–10 (2009)CrossRefGoogle Scholar
  6. 6.
    Xia, X., Maliski, E.G., Gallant, P., Rogers, D.: Classification of kinase inhibitors using a Bayesian model. J. Med. Chem. 47, 4463–4470 (2004)CrossRefGoogle Scholar
  7. 7.
    Winkler, D., Burden, F.R.: Application of neural networks to large dataset QSAR, virtual screening, and library design. Methods Mol. Biol. 201, 325–367 (2002)Google Scholar
  8. 8.
    Kawai, K., Fujishima, S., Takahashi, Y.: Predictive activity profiling of drugs by topological-fragment-spectra-based support vector machines. J. Chem. Inf. Model. 48(6), 1152–1160 (2008)CrossRefGoogle Scholar
  9. 9.
    LeCun, Y., Yoshua, B., Geoffrey, H.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  10. 10.
    Bengio, Y.: Learning deep architectures for AI, vol. 2, no. 1. (2009)Google Scholar
  11. 11.
    Gatys, L. Ecker, A.S. Bethge, M.: A Neural algorithm of artistic style. arXiv Prepr., pp. 1–16 (2015)Google Scholar
  12. 12.
    Wang, H., Meghawat, A., Morency, L.P., Xing E.P.: Select-additive learning: improving cross-individual generalization in multimodal sentiment analysis, vol. 1 (2016)Google Scholar
  13. 13.
    Hentabli, H., Naomie, S., Saeed, F.: An activity prediction model using shape-based descriptor method. J. Teknol. 1, 1–8 (2016)Google Scholar
  14. 14.
    Feldman, H.J., Dumontier, M., Ling, S., Haider, N., Hogue, C.W.V.: CO: a chemical ontology for identification of functional groups and semantic comparison of small molecules. FEBS Lett. 579(21), 4685–4691 (2005)CrossRefGoogle Scholar
  15. 15.
    Bobach, C., Böhme, T., Laube, U., Püschel, A., Weber, L.: Automated compound classification using a chemical ontology. J. Cheminform. 4(12), 1–12 (2012)Google Scholar
  16. 16.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality, pp. 1–9 (2013)Google Scholar
  17. 17.
    Bento, A.P.: The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42(D1), D1083–D1090 (2014)CrossRefGoogle Scholar
  18. 18.
    Lewis, R.A., Wood, D.: Modern 2D QSAR for drug discovery. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4(6), 505–522 (2014)CrossRefGoogle Scholar
  19. 19.
    Saeed, F., Salim, N.: Using soft consensus clustering for combining multiple clusterings of chemical structures. J. Teknol. Sci. Eng. 63(1), 9–11 (2013)Google Scholar
  20. 20.
    Hentabli, H., Saeed, F., Abdo, A., Salim, N.: A new graph-based molecular descriptor using the canonical representation of the molecule. Sci. World J. 2014 (2014)Google Scholar
  21. 21.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1–9 (2012)Google Scholar
  22. 22.
    Gupta, V.: Image classification using convolutional neural networks in keras (2017)Google Scholar
  23. 23.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)Google Scholar
  24. 24.
    John, G.H. Langley, P.: Estimating continuous distributions in Bayesian classifiers, pp. 338–345 (2013)Google Scholar
  25. 25.
    Chang, C.C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 271–2727 (2011)CrossRefGoogle Scholar
  26. 26.
    Bugmann, G.: Normalized Gaussian radial basis function networks. Neurocomputing 20(1–3), 97–110 (1998)CrossRefGoogle Scholar
  27. 27.
    Bastien, F.: Theano: new features and speed improvements, pp. 1–10 (2012)Google Scholar
  28. 28.
    Chollet, F.: Keras documentation. Keras.Io (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hentabli Hamza
    • 1
    Email author
  • Maged Nasser
    • 1
  • Naomie Salim
    • 1
  • Faisal Saeed
    • 2
  1. 1.School of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia
  2. 2.College of Computer Science and EngineeringTaibah UniversityMedinaSaudi Arabia

Personalised recommendations