Abstract
Molecular Docking faces problems related to Curse of dimensionality, due to the fact that it analyzes data with high dimensionality and few samples. (Ligand-Based Virtual Screening) conducts studies of docking among molecules using common attributes registered in data bases. This branch of Molecular Docking, uses Optimization methods and Machine learning algorithms in order to discover molecules similar to known drugs and can be proposed as drug candidates. Such algorithms are affected by effects of Curse of dimensionality. It this paper we propose to use LMC complexity measure (Complexity of Lopez-Ruiz, Mancini, and Calbet) [1] as similarity measurement among vectors in order to discover the best molecules to be drugs; and present an algorithm, which evaluates the similarity among vectors using this concept. The results suggest that application of this concept on Drug Example vectors; in order to classify other vectors as drugs candidates which is more informative than individually searching for vectors. Since the Drug Examples show a global similarity degree with drug candidate vectors. The aforementioned similarity degree makes it possible to deduce which elements of the Drug Examples show higher degree of similarity with drug candidates. Searching of vectors through individual comparison with Drug Examples was less efficient, because their classification is affected by the Drug Examples with a higher number of global discrepancies. Finally, the proposed algorithm avoids some of the Curse of dimensionality effects by using a ranking process where the best drug candidate vectors are those with the lowest complexity.
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We are grateful to DuPont Pharmaceuticals Research Laboratories and KDD Cup 2001 by provided this data set through UCI Machine Learning Repository.
References
Shiner, J.S., Davison, M., Landsberg, P.T.: Simple measure for complexity. Phys. Rev. E 59(2), 1459 (1999)
Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins: Struct. Funct. Bioinf. 47(4), 409–443 (2002)
Teodoro, M.L., Phillips, G.N.: Molecular docking: a problem with thousands of degrees of freedom. In: IEEE International Conference on Robotics and Automation, pp. 960–966 (2001)
Kearsley, S.K., Sheridan, R.P.: Why do we need so many chemical similarity search methods? Drug Discov. Today 7(17), 903–911 (2002)
Karthikeyan, M., Vyas, R.: Practical Chemoinformatics. Springer, New Delhi (2014). doi:10.1007/978-81-322-1780-0
Lavecchia, A., Di Giovanni, C.: Virtual screening strategies in drug discovery: a critical review. Curr. Med. Chem. 20(23), 2839–2860 (2013)
Zheng, M., Liu, Z., Yan, X., Ding, Q., Gu, Q., Xu, J.: LBVS: an online platform for ligand-based virtual screening using publicly accessible databases. Mol. Divers. 18(4), 829–840 (2014)
Nicolaou, C.A., Brown, N.: Multi-objective optimization methods in drug design. Drug Discov. Today, 30(20) (2013)
Clarke, R., Ressom, H.W., et al.: The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8, 13 (2008)
Lavecchia, A.: Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 20(3), 318–331 (2015)
Danishuddin, M., Khan, A.U.: Virtual screening strategies: a state of art to combat with multiple drug resistance strains. MOJ Proteomics Bioinform
Shan, S., Wang, G.G.: Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Struct. Multidiscip. Optim. 41(2), 219–241 (2010)
Sousa, S.F., Ribeiro, A.J.M., Coimbra, J.T.S., Neves, R.P.P., Martins, S.A., Moorthy, N.S.H.N., Fernandes, P.A., Ramos, M.J.: Protein-ligand docking in the new millennium-a retrospective of 10 years in the field. Curr. Med. Chem. 20(18), 2296–2314 (2013)
Li, Q., Cheng, T., Wang, Y., Bryant, S.H.: PubChem as a public resource for drug discovery. Drug Discov. Today 15(23), 1052–1057 (2010)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 10–12 (1948)
Crutchfield, J.P.: Between order and chaos. Nat. Phys. 8(1), 17–24 (2012)
Lopez-Ruiz, R., Mancini, H., Calbet, X.: A statistical measure of complexity. arXiv preprint nlin/0205033 (2002)
Grünwald, P.D., Vitányi, P.M.B.: Kolmogorov complexity and information theory. With an interpretation in terms of questions and answers. J. Logic Lang. Inform. 12(4), 497–529 (2003)
Seaward, L., Matwin, S.: Intrinsic plagiarism detection using complexity analysis. In: Proceedings of the SEPLN, pp. 56–61 (2009)
Feldman, D.P., Crutchfield, J.P.: Measures of statistical complexity: why? Phys. Lett. A 238(4), 244–252 (1998)
DuPont Pharmaceuticals Research Laboratories. Dorothea data set
Zhang, W., Ji, L., Chen, Y., Tang, K., Wang, H., Zhu, R., Jia, W., Cao, Z., Liu, Q.: When drug discovery meets web search: learning to rank for ligand-based virtual screening. J. Cheminform. 7, 5 (2015)
Kurczab, R., Smusz, S., Bojarski, A.J.: Evaluation of different machine learning methods for ligand-based virtual screening. J. Cheminform. 3(S–1), 41 (2011)
Tanrikulu, Y., Krüger, B., Proschak, E.: The holistic integration of virtual screening in drug discovery. Drug Discov. Today 18(7), 358–364 (2013)
Klebe, G.: Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11(13), 580–594 (2006)
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Martínez Medina, M., González-Mendoza, M., Hernández Gress, N. (2017). Molecular Docking Based on Ligand by Complexity LMC . In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_34
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