Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 pp 101–110Cite as

Predicting Biological Activity of 2,4,6-trisubstituted 1,3,5-triazines Using Random Forest

  • Conference paper

  • 1135 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 303))

Abstract

This paper presents an approach to predict the activity of analogues of 2,4,6-trisubstituted 1,3,5-triazines as cannabinoid receptor (CB2) agonists using random forest technique. We compute twenty molecular descriptors for a data set of 58 analogues for the component, and depending on values of these descriptors we train random forest to find a relation between biological activity and molecular structure of analogues. The results obtained by random forest were compared with the decision tree and support vector machine classifiers and the random forest has 100% overall predicting accuracy and for decision tree and support vector machine were 93% and 67% respectively.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Yrj, S., Kalliokoski, T., Laitinen, T., Poso, A., Parkkari, T., Nevalainen, T.: Discovery of novel cannabinoid receptor ligands by a virtual screening approach: Further development of 2,4,6-trisubstituted 1,3,5-triazines as CB2 agonists. Eur. J. Pharm. Sci. 48(1-2), 9–20 (2013)

    Article  Google Scholar 

  2. Gutman, I., Rusci, B., Trinajsti, N., Wilcox, C.F.: Graph theory and molecular orbitals. XII. Acyclic polyenes. J. Chem. Phys. 62, 3399–3405 (1975)

    Article  Google Scholar 

  3. Kier, L.B., Hall, L.H.: Molecular Connectivity in Structure-Activity Analysis. RSP-Wiley, Chetster (1986)

    Google Scholar 

  4. Sharma, V., Goswami, R., Madan, A.K.: Eccentric connectivity index: A novel highly discriminating topological descriptor for structure-property and structure-activity studies. J. Chem. Inf. Comput. Sci. 37, 273–282 (1997)

    Article  Google Scholar 

  5. Gupta, S., Singh, M., Madan, A.K.: Superpendentic index: A novel topological descriptor for predicting biological activity. J. Chem. Inf. Comput. Sci. 39, 272–277 (1999)

    Article  Google Scholar 

  6. Wiener, H.: Correlation of heat of isomerization and difference in heat of vaporization of isomers among paraffin hydrocarbons. J. Am. Chem. Soc. 69, 17–20 (1947)

    Article  Google Scholar 

  7. Todeschini, R., Consonni, V.: Molecular Descriptors for Chemoinformatics, vol. 1, pp. 1–955. Wiley VCH, Weinheim (2009)

    Google Scholar 

  8. Dutt, R., Madan, A.K.: Predicting biological activity: Computational approach using novel distance based molecular descriptors. Computers in Biology and Medicine 42, 1026–1041 (2012)

    Article  Google Scholar 

  9. Tetko, I.V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V.A., Radchenko, E.V., Zefirov, N.S., Makarenko, A.S., Tanchuk, V.Y., Prokopenko, V.V.: Virtual computational chemistry laboratory-design and description. J. Comput.-Aided Mol. Des. 19, 453–463 (2005)

    Article  Google Scholar 

  10. Cios, K., Pedrycz, W., Swiniarski, R., Kurgan, L.: Data Mining: A Knowledge Discovery Approach. Springer (2007)

    Google Scholar 

  11. Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43, 1947–1958 (2003)

    Article  Google Scholar 

  12. Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  13. Zhang, Q.-U., Aires-de-Sousa, J.: Random Forest Prediction of Mutagenicity from Empirical Physicochemical Descriptors. J. Chem. Inf. Mod. 47, 1–8 (2007)

    Article  Google Scholar 

  14. Prasad, A.M., Iverson, L.R., Liaw, A.: Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems 9, 181–199 (2006)

    Article  Google Scholar 

  15. Guha, R., Jurs, P.C.: Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. J. Chem. Inf. Comp. Sci. 44, 2179–2189 (2004)

    Article  Google Scholar 

  16. Han, L., Wang, Y., Bryant, S.H.: Developing and validating predictive decision tree models from mining chemical structural fingerprints and high through- output data in PubChem. BMC Bioinformat. 9, 401 (2008)

    Article  Google Scholar 

  17. Lamanna, C., Bellini, M., Padova, A., Westerberg, G., Maccari, L.: Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. J. Med. Chem. 51, 2891–2897 (2008)

    Article  Google Scholar 

  18. Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage Lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975)

    Article  Google Scholar 

  19. Goyal, R.K., Dureja, H., Singh, G., Madan, A.K.: Models for Antitubercular Activity of 5’-O-[(N-Acyl)sulfamoyl]adenosines. Scientia Pharmaceutica 78, 791–820 (2010)

    Article  Google Scholar 

  20. Das, K.C., Gutman, I.: Some properties of the second Zagreb index. Match. Commun. Math. Comput. Chem. 52, 103–112 (2004)

    MATH  MathSciNet  Google Scholar 

  21. Todeschini, R., Gramatica, P.: New 3D molecular descriptors: the WHIM theory and QSAR applications. Perspect. Drug Discov. Des. 2, 355–380 (1998)

    Article  Google Scholar 

  22. K. S, D.-W. M, and P. N.: Trends in drug development time and price. Abstracts of Academy Health Meetings 22, 36–76 (2005)

    Google Scholar 

  23. Lewis, R.A.: A general method for exploiting QSAR models in lead optimization. J. Med. Chem. 48, 1638–1648 (2005)

    Article  Google Scholar 

  24. Turner, J.V., Maddalena, D.J., Cutler, D.J.: Pharmacokinetic parameter prediction from drug structure using artificial neural networks. Int. J. Pharm. 270, 209–219 (2004)

    Article  Google Scholar 

  25. Matsuda, L.A., Lolait, S.J., Brownstein, M.J., Young, A.C., Bonner, T.I.: Structure of a cannabinoid receptor and functional expression of the cloned cDNA. Nature 346, 561–564 (1990)

    Article  Google Scholar 

  26. Munro, S., Thomas, K.L., Abu-Shaar, M.: Molecular characterization of a peripheral receptor for cannabinoids. Nature 365, 61–65 (2003)

    Article  Google Scholar 

  27. Pacher, P., Batkai, S., Kunos, G.: The endocannabinoid system as an emerging target of pharmacotherapy. Pharmacol. Rev. 58, 389–462 (2006)

    Article  Google Scholar 

  28. Basavarajappa, B.S.: Neuropharmacology of the endocannabinoid signaling system-molecular mechanisms, biological actions and synaptic plasticity. Curr. Neuropharmacol. 5, 81–97 (2007)

    Article  Google Scholar 

  29. Yates, M.L., Barker, E.L.: Inactivation and biotransformation of the endogenous cannabinoids anandamide and 2-arachidonoylglycerol. Mol. Pharmacol. 76, 11–17 (2009)

    Article  Google Scholar 

  30. Di Marzo, V.: Targeting the endocannabinoid system: to enhance or reduce. Nat. Rev. Drug. Discov. 7, 438–455 (2008)

    Article  Google Scholar 

  31. Pacher, P., Mechoulam, R.: Is lipid signaling through cannabinoid 2 receptors part of a protective system. Prog. Lipid Res. 50, 193–211 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Scientific Research Group in Egypt (SRGE), egyptscience.net, Cairo, Egypt

    Ahmed H. Abu El-Atta & Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmed H. Abu El-Atta & M. I. Moussa

  3. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

Authors
  1. Ahmed H. Abu El-Atta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. I. Moussa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science Faculty of Elec. Eng. and Comp. Sci., VŠB-TUO, Ostrava-Poruba, Czech Republic

    Pavel Kömer

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Washington, Alabama, USA

    Ajith Abraham

  3. Department of Computer Science Faculty of Elect. Eng. and Comp. Sci., VŠB-TUO, Ostrava-Poruba, Czech Republic

    Václav Snášel

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

El-Atta, A.H.A., Moussa, M.I., Hassanien, A.E. (2014). Predicting Biological Activity of 2,4,6-trisubstituted 1,3,5-triazines Using Random Forest. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08156-4_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08155-7

  • Online ISBN: 978-3-319-08156-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation