Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We are grateful to DuPont Pharmaceuticals Research Laboratories and KDD Cup 2001 by provided this data set through UCI Machine Learning Repository.
References
Bellman, R.: On the theory of dynamic programming. Proc. Natl. Acad. Sci. 38(8), 716–719 (1952)
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)
Lan, F.: The discriminate analysis and dimension reduction methods of high dimension. Open J. Soc. Sci. 3(03), 7 (2015)
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)
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)
Phyu, T.Z., Oo, N.N.: Performance comparison of feature selection methods. In: MATEC Web of Conferences, vol. 42. EDP Sciences (2016)
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)
Sheridan, R.P., Kearsley, S.K.: Why do we need so many chemical similarity search methods? Drug Discov. Today 7(17), 903–911 (2002)
Nicolaou, C.A., Brown, N.: Multi-objective optimization methods in drug design. Drug Discov. Today: Technol. 10(3), e427–e435 (2013)
Lavecchia, A.: Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 20(3), 318–331 (2015)
Lill, M.: Virtual screening in drug design. In: Kortagere, S. (ed.) In Silico Models for Drug Discovery, pp. 1–12. Humana Press, Totowa (2013). https://doi.org/10.1007/978-1-62703-342-8_1
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)
Eckert, H., Bajorath, J.: Molecular similarity analysis in virtual screening: foundations, limitations. Drug Discov. Today 12(5), 225–233 (2007)
SaiKrishna, V., Rasool, A., Khare, N.: String matching and its applications in diversified fields. Int. J. Comput. Sci. Issues 9(1), 219–226 (2012)
Köpcke, H., Rahm, E.: Frameworks for entity matching: a comparison. Data Knowl. Eng. 69(2), 197–210 (2010)
Minghe, Y., Li, G., Deng, D., Feng, J.: String similarity search and join: a survey. Front. Comput. Sci. 10(3), 399–417 (2016)
Garrid, A.: About some properties of the Kullback-Leibler divergence. Adv. Model. Optim. 11, 571–578 (2009)
Cichocki, A., Amari, S.: Families of alpha-beta-and gamma-divergences: flexible and robust measures of similarities. Entropy 12(6), 1532–1568 (2010)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Martínez-Medina, M., González-Mendoza, M., Herrera-Alcántara, O. (2018). Jeffrey Divergence Applied to Docking Virtual. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-02837-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02836-7
Online ISBN: 978-3-030-02837-4
eBook Packages: Computer ScienceComputer Science (R0)