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Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs

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Abstract

Designing drugs is a current problem in the pharmaceutical research. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose an ensemble of three learning algorithms namely an evolutionary artificial neural network, Takagi-Sugeno neuro-fuzzy system and an artificial neural network. The ensemble combination is optimized by a particle swarm optimization algorithm. The experimental data were obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj-Napoca, Romania. Bootstrap techniques were used to generate more samples of data since the number of experimental data was low due to the costs and time durations of experimentations. Experiment results indicate that the proposed methods are efficient.

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References

  1. Parrill AL (1996) Evolutionary and genetic methods in drug design. Drug Discov Today 1(12):514–521

    Article  Google Scholar 

  2. Zoubir AM, Iskander DR (1998) Bootstrap MATLAB Toolbox. Software reference manual

  3. Abraham A (2004) Meta-learning evolutionary artificial neural networks. Neurocomp J 56c:1–38

    Article  Google Scholar 

  4. Abraham A (2001) Neuro-fuzzy systems: state-of-the-art modeling techniques, connectionist models of neurons, learning processes, and artificial intelligence. In: Mira J, Prieto A (eds.) Lecture notes in computer science. Springer, Berlin, LNCS 2084, pp 269–276

  5. Barat A, Ruskin HJ, Crane M (2006) Probabilistic models for drug dissolution. Part 1. Review of Monte Carlo and stochastic cellular automata approaches. Simul Model Pract Theory 14(7):843–856

    Article  Google Scholar 

  6. Laghaee A, Malcolm C, Hallam J, Ghazal P (2005) Artificial intelligence and robotics in high throughput post-genomics. Drug Discov Today 10(18):1253–1259

    Article  Google Scholar 

  7. Carlsson C, Fullér R (1998) Multiobjective optimization with linguistic variables. In: Proceedings of the sixth European congress on intelligent techniques and soft computing, Aachen, September 7–10, 1998, Verlag Mainz

  8. Winkler DA, Burden FR (2004) Bayesian neural nets for modeling in drug discovery. Drug Discov Today: BIOSILICO 2(3):104–111

    Article  Google Scholar 

  9. Aradi I, Erdi P (2006) Computational neuropharmacology: dynamical approaches in drug discovery. Trends Pharmacol Sci 27(5):240–243

    Article  Google Scholar 

  10. Carpenter J, Goldstein H, Rasbash J (1999) A non-parametric bootstrap for multilevel models. Multilevel Model Newsl 11:2–5

    Google Scholar 

  11. Jang SR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Englewood Cliffs

  12. DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J Health Econ 22:151–185

    Article  Google Scholar 

  13. Takayama K, Fujikawa M, Obata Y, Morishita M (2003) Neural network based optimization of drug formulations. Adv Drug Deliv Rev 55(9):1217–1231

    Article  Google Scholar 

  14. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Vol. IV, pp 1942–1948. IEEE service center, Piscataway, NJ

  15. Stewart KD, Shiroda M, James CA (2006) Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorg Med Chem 14(20):7011–7022

    Article  Google Scholar 

  16. Hu L, Chen GH, Chau RMW (2006) A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors. J Mol Graph Model 24(4):244–253

    Article  Google Scholar 

  17. Teroth L, Gasteiger J (2001) Neural networks and genetic algorithms in drug design. Drug Discov Today 6(2):102–108

    Article  Google Scholar 

  18. Moller AF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  19. Meek PJ, Liu Z, Tian L, Wang CY, Welsh WJ, Zauhar RJ (2006) Shape signatures: speeding up computer aided drug discovery. Drug Discov Today 11(19–20):895–904

    Article  Google Scholar 

  20. Esseiva P, Anglada F, Dujourdy L, Taroni F, Margot P, Pasquier ED, Dawson M, Roux C, Doble P (2005) Chemical profiling and classification of illicit heroin by principal component analysis, calculation of inter sample correlation and artificial neural networks. Talanta 67(2):360–367

    Article  Google Scholar 

  21. Burbidge R, Trotter M, Buxton B, Holden S (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26(1):5–14

    Article  Google Scholar 

  22. Câmpean R, Prodan A (2003) Biomatematică – aplicaţii în Excel, Editura Medicală Universitară “Iuliu Haţieganu”, Cluj-Napoca, ISBN: 973-693-016-5

  23. Câmpean R, Prodan A (2003) A rating model applied for designing drugs. In: Proceedings of the 12-th IASTED international conference on applied simulation and modelling, Marbella, Spain, pp 557–561, ACTA press, ISBN: 0-88986-384-9, ISSN: 1021–8181

  24. Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727

    Article  Google Scholar 

  25. Hesterberg T, Monaghan S, Moore DS, Clipson A, Epstein R (2003) Bootstrap methods and permutation tests. W. H. Freeman and Company, New York

    Google Scholar 

  26. Solmajer T, Zupan J (2004) Optimization algorithms and natural computing in drug discovery. Drug Discov Today: Technol 1(3):247–252

    Article  Google Scholar 

  27. Kiss T, Érdi P (2006) From electric patterns to drugs: perspectives of computational neuroscience in drug design. Biosystems 86(1–3):46–52

    Article  Google Scholar 

  28. Sun Y, Peng Y, Chen Y, Shukla AJ (2003) Application of artificial neural networks in the design of controlled release drug delivery systems. Adv Drug Deliv Rev 55(9):1201–1215

    Article  Google Scholar 

  29. Tang Y, Zhu W, Chen K, Jiang H (2006) New technologies in computer-aided drug design: toward target identification and new chemical entity discovery. Drug Discov Today: Technol 3(3):307–313

    Google Scholar 

  30. Grosan C, Abraham A, Tigan S (2006) Engineering drug design using a multi-input multi-output neuro-fuzzy system, 8th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC'06), Timisoara, Romania, IEEE CS Press, pp 365–371

  31. Grosan C, Abraham A, Tigan S, Chang T-G, Kim DH (2006) Evolving neural networks for pharmaceutical research, International conference on hybrid information technology (ICHIT'06), IEEE Press, Korea, pp 13–19

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Acknowledgments

Authors would also like to thank the colleagues of the Department of Maxillofacial Surgery, University of Medicine and Pharmacy, Iuliu Hatieganu Cluj-Napoca, for the initial contributions of this research

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Authors and Affiliations

  1. Faculty of Information Technology, Mathematics and Electrical Engineering, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham & Crina Grosan

  2. Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, 3400, Romania

    Crina Grosan

  3. Faculty of Medicine, Department of Biostatistics and Medical Informatics, University Iuliu Haţieganu, Cluj-Napoca, Romania

    Ştefan Ţigan

Authors
  1. Ajith Abraham
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  2. Crina Grosan
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  3. Ştefan Ţigan
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Correspondence to Ajith Abraham.

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Abraham, A., Grosan, C. & Ţigan, Ş. Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs. Neural Comput & Applic 16, 307–316 (2007). https://doi.org/10.1007/s00521-007-0090-1

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  • DOI: https://doi.org/10.1007/s00521-007-0090-1

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