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Reseach Article

Role of the Computational Intelligence in Drugs Discovery and Design: Introduction, Techniques and Software

by Geeta Yadav, Yugal Kumar, G. Sahoo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 10
Year of Publication: 2012
Authors: Geeta Yadav, Yugal Kumar, G. Sahoo

Geeta Yadav, Yugal Kumar, G. Sahoo . Role of the Computational Intelligence in Drugs Discovery and Design: Introduction, Techniques and Software. International Journal of Computer Applications. 51, 10 ( August 2012), 7-18. DOI=10.5120/8076-1476

@article{ 10.5120/8076-1476,
author = { Geeta Yadav, Yugal Kumar, G. Sahoo },
title = { Role of the Computational Intelligence in Drugs Discovery and Design: Introduction, Techniques and Software },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 10 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-18 },
numpages = {9},
url = { },
doi = { 10.5120/8076-1476 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:50:01.540874+05:30
%A Geeta Yadav
%A Yugal Kumar
%A G. Sahoo
%T Role of the Computational Intelligence in Drugs Discovery and Design: Introduction, Techniques and Software
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 10
%P 7-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Drugs discovery & design is an intense, lengthy and consecutive process that starts with the lead & target discovery followed by lead optimization and pre-clinical in vitro & in vivo studies. This paper throws light on different computational techniques that play a vital role in the drugs discovery & design process. Earlier, computational techniques are use in the field of computer science, electrical engineering and electronics & communication engineering to solve the problems. But, now day's use of these techniques has changed the scenario in drugs discovery. & design from the last two decades. This paper present brief description of different computational techniques such as Particle Swarm Optimization, Ant Colony Optimization, Artificial Neural Network, Fuzzy logic, Genetic Algorithm, Genetic Programming, Evolutionary Programming, Evolutionary Strategy and also provide a tabular comparison of these techniques as well as a list of computational tools/ software.

  1. DiMasi, J. A. et al. , "The price of innovation: new estimates of drug development costs", J. Health Economics 20023,22, 151–185.
  2. Drug Discovery and Development: Understand the R&D Process from innovative. org.
  3. Jorgensen W. L. , "The many roles of computation in drug discovery", Science 303, 2004, 1813–1818.
  4. Lothar Terfloth and Johann Gasteiger," Neural networks and genetic algorithms in drug design", Journal of Drugs discovery Today, 2001, Vol. 6, No. 12 (Suppl. ) pp no. 102-108.
  5. Forschungsbericht erstellt von and Peter von Bülow," A Survey and Comparison of Biological Genetics and Evolutionary Computation", june 2007 (Thesis).
  6. Koza, JR. , "Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems", Technical Report STANCS-90-1314, Department of Computer Science, Stanford University, June 1990.
  7. Koza JR. "Genetic Programming", Cambridge, MA: MIT Press, 1999.
  8. Rechenberg I. , 'Evolutions strategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution', 1973(Phd Thesis).
  9. Schwefel H. P. , 'Numerical Optimization of Computer Models', Wiley, Chichester, 1981.
  10. Back, Hoffmeister and Schwefel. "A Survey of Evolution Strategies", in Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann Publ. , San Mateo, California, 1992, pp. no. 2-9.
  11. Fogel L. J. , Owens A. J. , and Walsh M. J. , 'Artificial Intelligence through Simulated Evolution', Wiley, New York, 1966 (Book).
  12. Fogel L. J. , "Autonomous automata" Ind Res vol. 4, 1962, pp. no. 14–19.
  13. Carlos Andre´s, Pena Reyes, Moshe Sipper," Evolutionary computation in medicine: an overview", Artificial Intelligence in Medicine 19 (2000), pp. no. 1–23.
  14. Beth A. Sproule, Claudio A. Naranjo and I. Burhan Türksen," Fuzzy pharmacology: theory and applications", TRENDS in Pharmacological Sciences Vol. 23 No. 9, 2002, pp. no. 412-417.
  15. Zadeh, L. A. "Fuzzy sets", Information and Control 8 (3), 1965, pp. no. 338–353.
  16. Zadeh L. A. ," Outline of a new approach to the analysis of complex systems and decision processes" IEEE Trans. Syst. Man Cybern. 3, 1973 , pp. no. 28 -44.
  17. Mohit Kumar, Kerstin Thurow, Norbert Stoll , Regina Stoll," Robust fuzzy mappings for QSAR studies", European Journal of Medicinal Chemistry published by Elsevier 42, 2007, 675-685.
  19. McCulloch W. S. and Pitts W. , "A logical calculus of the ideas immanent in nervous activity", Bull. Math Biophys (5), 1943, pp. no. 115-133.
  20. Rosenblatt, F. Principles of neurodynamics: perceptrons and thetheory of brain mechanisms, Washington, DC: Spartan 1962(Books).
  21. Haykin, S. Neural networks: A nomprehensive foundation, Upper Saddle River, NJ: Prentice Hall, 1998.
  22. Gisbert Schneider and Paul Wrede, "Artificial neural networks for computer-based molecular design", Progress in Biophysics & Molecular Biology 70 (1998) 175- 222.
  23. Eberhart and Kennedy," Particle swarm optimization", Proc. IEEE international conf. on neural networks – IV, 1995, pp. no. 1942 - 1948.
  24. Couzin ID, Krause J, James R, Ruxton GD, Franks NR, "Collective Memory and Spatial Sorting in Animal Groups", Journal of Theoretical Biology, 2002, 218, pp. 1–11.
  25. Grosan C, Abraham A and Monica C, "Swarm Intelligence in Data Mining", SCI vol. 34, 2006, Springer, pp. 1–16.
  26. Swagatam Das, Ajith Abraham*, and Amit Konar," Swarm Intelligence Algorithms in Bioinformatics", Swarm Intelligence Algorithms in Bioinformatics, Studies in Computational Intelligence (SCI) 94, 2008, 113–147.
  27. Chuang LY, Chang HW, Tu CJ, Yang CH,"Improved bi nary PSO for feature selection using gene expression data", Comput Biol Chem32, 2008, 29–38.
  28. Shen Q, ShiWM, Kong W, Ye BX,"A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification", Talanta 71, 2007, 1679–1683.
  29. Call ST, Zubarev DY, Boldyrev AI,"Global minimum structure searches via particle swarm optimization", J Comput Chem 28, 2007, 1177–1186.
  30. Namasivayam V, Günther R,"PSO@AUTODOCK: A fast flexible molecular docking program based on swarm intelligence", Chem. Biol. Drug Des 70, 2007, 475–484.
  31. Agrafiotis DK, Cedeño W (2002),"Feature selection for structure-activity correlation using binary particle swarms", J Med Chem. 45, 2002, 1098–1107.
  32. Chang BCH, Ratnaweera A, Halgamuge SK,Watson HC, "Particle swarm optimisation for protein motif discovery", Genetic Programming Evolv. 5, 2004, 203–214.
  33. Marc De Jonge, Luc Koymans, Maarten Vinkers, "An Ant Algorithm for the Conformational Analysis of Flexible Molecules", Journal of Computational Chemistry vol. 28 issue 5, 2007, pp. no. 890 - 898.
  34. Zhiwei Wang, Gregory L. Durst and Russell C. Eberhart," Particle Swarm Optimization and Neural Network Application for QSAR", 18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 9, vol 10, 2004, pp 195.
  35. Hoffman, B. T. et al. ,"2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors", J. Med. Chem. 43, 2000, pp. 4151–4159.
  36. Turner, D. B. and Willett, P, "Evaluation of the EVA descriptor for QSAR studies: The use of a genetic algorithm to search for models with enhanced predictive properties", J. Computational Aided Mol. Design 14, 2000, pp. 1–21.
  37. Kimura T. et al. (1998) ,"GA strategy for variable selection in QSAR studies: GA-based region selection for CoMFA modeling", J. Chem. Inf. Comput. Sci. 38, 1998, 276–282.
  38. Hasegawa, K. et al. ,"GA strategy for variable selection in QSAR studies: application of GA-based region selection to a 3D- QSAR study of acetylcholinesterase inhibitors" J. Chem. Inf. Comput. Sci. 39, 1999, 112–120.
  39. James Cunha Werner and Terence C. Fogarty," Genetic programming applied to pharmaceutical drugs design", The Seventh ACM SIGKDD International Conference on Knowledge discovery and data mining, USA/ San Francisco, August 26-29,2001.
  40. Schneider G. , Schuchhardt J. , and Wrede P. , Comput. , "Artificial Neural network and simulated molecular evolution are potential tool for sequence oriented protein design", Appl. Biosci. vol. 6, 1994, pp. 635 645.
  41. Fogel D. B. , 'Evolutionary Computation: Toward a New Philosophy of Machine Intelligence', IEEE Press, Piscataway, 2006 (Book).
  42. Luke B. T. ,"Evolutionary programming applied to the devlopement of QSAR and QSPR", J. Chem. Inf. Comput. Sci. , 34, 1994, 1279 1287.
  43. Gehlhaar D. K. , Verkhivker G. M. , Rejto P. A. , Sherman C. J. , Fogel D. B. , Fogel L. G. , and Freer S. T. , "Molecular recoginition of the inhabibitor AG:1343 by HIV Protease: Conformationallhy flexible docking by evolutionary Programming", Chem. Biol. Vol 2, 1995, pp. 317 324.
  44. Huuskonen, J. et al. ,"Aqueous solubility prediction of drugs based on molecular topology and neural network modeling", J. Chem. Inf. Comput. Sci. 38, 1998, 450–456.
  45. Schneider, G. et al. ,"Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques", J. Med. Chem. 42, 1999, 5072–5076
  46. Jalali-Heravi M. and Parastar, F. (2000),"Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives", J. Chem. Inf. Comput. Sci. 40, 2000, 147–154.
  47. Burden, F. R. and Winkler, D. A. (1999), "Robust QSAR models using Bayesian regularized neural networks", J. Med. Chem. 42, 1999, 3183–3187
  48. Burden, F. R. et al. , "Use of automatic relevance determination in QSAR studies using Bayesian neural networks", J. Chem. Inf. Comput. Sci. 40, 2000, 1423–1430.
  49. Angela Torres and Juan J. Nieto," Fuzzy Logic in Medicine and Bioinformatics", journal of Biomedicine and Biotechnology, 2006, Pages 1–7.
  50. Papageorgiou EI, Stylios CD, Groumpos PP. "An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps", IEEE Transactions on Biomedical Engineering 50(12), 2003, pp. 1326–1339.
  51. Oshita S, Nakakimura K, Sakabe T. ,"Hypertension control during anesthesia - Fuzzy logic regulation of nicardipine infusion", IEEE Engineering in Medicine and Biology Magazine 13(5), 1994, 667–670.
  52. Johnson M, Firoozbakhsh K, Moniem M, Jamshidi M. ,"Determining flexor-tendon repair techniques via soft computing", IEEE Engineering in Medicine and Biology Magazine 20(6), 2001, pp. 176–183.
  53. Hassanien AE. ,"Intelligent data analysis of breast cancer based on rough set theory", International Journal on Artificial Intelligence Tools. 12(4), 2003, pp. 465–479.
  54. Seker H, OdetayoMO, Petrovic D, Naguib RN. ,"A fuzzy logic based-method for prognostic decision making in breast and Prostate cancers", IEEE Transactions on Information Technology in Biomedicine 7(2) , 2003, 114–122.
  55. Schneider J, Peltri G, Bitterlich N, et al. ,"Fuzzy logic-based tumor marker profiles including a new marker tumor M2-PK Improved sensitivity to the detection of progression in lung cancer patients", Anticancer Research 23(2A, ) 2003, 899–906.
Index Terms

Computer Science
Information Sciences


Biological Inspiration Computational Techniques Fitness Function Programming Optimization