INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.11, No.3, Mar. 2019

Multi-objective Monkey Algorithm for Drug Design

Full Text (PDF, 684KB), PP.31-41


Views:2   Downloads:0

Author(s)

R. Vasundhara Devi, S. Siva Sathya, Nilabh Kumar, Mohane Selvaraj Coumar

Index Terms

Swarm intelligence algorithm;Monkey algorithm;De novo drug design;Single objective optimization;Multi-objective optimization

Abstract

Swarm intelligence algorithms are designed to mimic the natural behaviors of living organisms. The birds, animals and insects exhibit extraordinary problem solving behaviors and intelligence when living in colonies or groups. These unique behaviors form the basis for the design of the Metaheuristic which are helpful in solving several real-life combinatorial optimization problems. Monkey algorithm is developed based on the unique behaviors of monkeys such as mountain and tree climbing, jumping, watching and somersaulting. This paper reports for the first time the design and development of Multi-objective Monkey Algorithm (MoMA) and its use for the design of molecules with optimal drug-like properties. Finally, the performance of the proposed MoMA for Drug design (MoMADrug) is compared with the previously disclosed Multi-objective Genetic algorithm (MoGADdrug) for the design of drug-like molecules.

Cite This Paper

R. Vasundhara Devi, S. Siva Sathya, Nilabh Kumar, Mohane Selvaraj Coumar, "Multi-objective Monkey Algorithm for Drug Design", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.3, pp.31-41, 2019. DOI: 10.5815/ijisa.2019.03.04

Reference

[1]R. Zhao, “Monkey algorithm for global numerical optimization,” J. Uncertain Syst., vol. 2, no. 3, pp. 165–176, 2008.

[2]R. V. Devi and S. S. Sathya, “Monkey Behavior Based Algorithms - A Survey,” Int. J. Intell. Syst. Appl., vol. 9, no. 12, pp. 67–86, 2017.

[3]M. Reyes-sierra and C. A. C. Coello, “Multi-Objective Particle Swarm Optimizers : A Survey of the State-of-the-Art,” Int. J. Comput. Intell. Res., vol. 2, no. 3, pp. 287–308, 2006.

[4]R. V. Devi, S. S. Sathya, and M. S. Coumar, “Evolutionary algorithms for de novo drug design – A survey,” Appl. Soft Comput., vol. 27, pp. 543–552, 2015.

[5]R. Vasundhara Devi, S. Siva Sathya, and M. S. Coumar, “Multi-objective genetic algorithm for De novo drug design (MoGADdrug),” Int. J. Bio-Inspired Comput. (under review).

[6]I. J. I. Systems, “Solving Economic Load Dispatch Problem Using Particle Swarm Optimization Technique,” no. November, pp. 12–18, 2012.

[7]D. J. Persis and T. P. Robert, “Reliable Mobile Ad-Hoc Network Routing Using Firefly Algorithm,” no. May, pp. 10–18, 2016.

[8]S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016.

[9]K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.

[10]C. A. C. Coello, “An Introduction to Multi-Objective Particle Swarm Optimizers,” Soft Comput. Ind. Appl., no. 103570, pp. 3–12, 2011.

[11]S. Mirjalili, S. Saremi, and S. Mohammad, “Multi-objective grey wolf optimizer : A novel algorithm for multi-criterion optimization,” Expert Syst. Appl., vol. 47, pp. 106–119, 2016.

[12]C. A. Nicolaou, J. Apostolakis, and C. S. Pattichis, “De novo drug design using multiobjective evolutionary graphs,” J. Chem. Inf. Model., vol. 49, no. 2, pp. 295–307, 2009.

[13]F. Daeyaert and M. W. Deem, “A pareto algorithm for efficient De Novo design of multi-functional molecules,” 2014.

[14]R. V. Devi, S. S. Sathya, and M. S. Coumar, “Multi- Objective Genetic Algorithm for De Novo Drug Design,” Int. J. Soft Comput. Eng., vol. 4, no. 2, pp. 92–96, 2014.

[15]J. C. Spall, “An overview of the simultaneous perturbation method for efficient optimization,” John Hopkins APL Tech. Dig., no. 19, pp. 482–492, 1998.

[16]A. Konak, D. W. Coit, and A. E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial,” Reliab. Eng. Syst. Saf., vol. 91, no. 9, pp. 992–1007, 2006.

[17]Julio, Richard Everson, and J. F. Alvarez-Benitez, “A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts,” in Evolutionary Multi-Criterion Optimization. Springer Berlin/Heidelberg, 2005, pp. 459–473.

[18]N. Brown, “Chemoinformatics—an introduction for computer scientists,” ACM Comput. Surv., vol. 41, no. 2, pp. 1–38, 2009.

[19]C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, “Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Develop ment Settings,” Adv. Drug Deliv. Rev., vol. 23, pp. 3–25, 1997.

[20]C. A. Lipinski, “Lead- and drug-like compounds: The rule-of-five revolution,” Drug Discovery Today: Technologies, vol. 1, no. 4. pp. 337–341, 2004.

[21]E. Pihan, L. Colliandre, J. F. Guichou, and D. Douguet, “E-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design,” Bioinformatics, vol. 28, no. 11, pp. 1540–1541, 2012.