Jeffrey Divergence Applied to Docking Virtual

  • Mauricio Martínez-MedinaEmail author
  • Miguel González-MendozaEmail author
  • Oscar Herrera-Alcántara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)


Data analysis with high dimensionality and few samples implies a set of problems related with the Curse of dimensionality phenomenon. Molecular Docking faces these kind problems to compare molecules by similarity. LBVS-Ligand-Based Virtual Screening conducts studies of docking among molecules using their common attributes registered in specialized databases. These attributes are represented by high dimensionality boolean vectors where an bit set indicates the presence of an specific attribute in the molecule, whereas a zero bit, its absence. The discovering of new drugs through the comparison of these vectors involves exhaustive processes of matching among the vectors. In this work, it is proposed the use of Jeffrey divergence as a similarity measurement in order to find the best approximate virtual docking between distinct molecules, to reduce the computation time, and offset some of Curse of dimensionality effects. The results suggest the application of Jeffrey divergence on discovering of candidates to drugs allow to identify the best approximate matching among them.


Molecular docking Ligand-based virtual screening Curse of dimensionality Jeffrey divergence Approximate matching Drug discovering 


  1. 1.
    Bellman, R.: On the theory of dynamic programming. Proc. Natl. Acad. Sci. 38(8), 716–719 (1952)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Clarke, R., et al.: The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8(1), 37 (2008)CrossRefGoogle Scholar
  3. 3.
    Lan, F.: The discriminate analysis and dimension reduction methods of high dimension. Open J. Soc. Sci. 3(03), 7 (2015)Google Scholar
  4. 4.
    Motoda, H., Liu, H.: Feature selection, extraction and construction. In: Communication of IICM (Institute of Information and Computing Machinery, Taiwan), vol. 5, pp. 67–72 (2002)Google Scholar
  5. 5.
    Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: Science and Information Conference (SAI), pp. 372–378. IEEE (2014)Google Scholar
  6. 6.
    Phyu, T.Z., Oo, N.N.: Performance comparison of feature selection methods. In: MATEC Web of Conferences, vol. 42. EDP Sciences (2016)CrossRefGoogle Scholar
  7. 7.
    Kim, S.-K., Goddard III, W.A.: Molecular-docking-based drug design and discovery: rational drug design for the subtype selective GPCR ligands. In: Applied Case Studies and Solutions in Molecular Docking-Based Drug Design, pp. 158–185. IGI Global (2016)Google Scholar
  8. 8.
    Sheridan, R.P., Kearsley, S.K.: Why do we need so many chemical similarity search methods? Drug Discov. Today 7(17), 903–911 (2002)CrossRefGoogle Scholar
  9. 9.
    Nicolaou, C.A., Brown, N.: Multi-objective optimization methods in drug design. Drug Discov. Today: Technol. 10(3), e427–e435 (2013)CrossRefGoogle Scholar
  10. 10.
    Lavecchia, A.: Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 20(3), 318–331 (2015)CrossRefGoogle Scholar
  11. 11.
    Lill, M.: Virtual screening in drug design. In: Kortagere, S. (ed.) In Silico Models for Drug Discovery, pp. 1–12. Humana Press, Totowa (2013). Scholar
  12. 12.
    Danishuddin, M., Khan, A.U.: Virtual screening strategies: a state of art to combat with multiple drug resistance strains. MOJ Proteomics Bioinform. 2(2), 00042 (2015)Google Scholar
  13. 13.
    Eckert, H., Bajorath, J.: Molecular similarity analysis in virtual screening: foundations, limitations. Drug Discov. Today 12(5), 225–233 (2007)CrossRefGoogle Scholar
  14. 14.
    SaiKrishna, V., Rasool, A., Khare, N.: String matching and its applications in diversified fields. Int. J. Comput. Sci. Issues 9(1), 219–226 (2012)Google Scholar
  15. 15.
    Köpcke, H., Rahm, E.: Frameworks for entity matching: a comparison. Data Knowl. Eng. 69(2), 197–210 (2010)CrossRefGoogle Scholar
  16. 16.
    Minghe, Y., Li, G., Deng, D., Feng, J.: String similarity search and join: a survey. Front. Comput. Sci. 10(3), 399–417 (2016)CrossRefGoogle Scholar
  17. 17.
    Garrid, A.: About some properties of the Kullback-Leibler divergence. Adv. Model. Optim. 11, 571–578 (2009)Google Scholar
  18. 18.
    Cichocki, A., Amari, S.: Families of alpha-beta-and gamma-divergences: flexible and robust measures of similarities. Entropy 12(6), 1532–1568 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Instituto Tecnológico y de Estudios Superiores de MonterreyMexicoMexico
  2. 2.Universidad Autónoma MetropolitanaMexicoMexico

Personalised recommendations