Top

Neural Computing and Applications

Published in:

01-05-2007 | Original Article

Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs

Authors: Ajith Abraham, Crina Grosan, Ştefan Ţigan

Published in: Neural Computing and Applications | Issue 3/2007

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article A data reduction approach for resolving the imbalanced data issue in functional genomics

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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

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

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

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

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):843–856CrossRef

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

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

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

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

10.

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

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 22:151–185CrossRef

13.

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

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):7011–7022CrossRef

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):244–253CrossRef

17.

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

18.

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

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):895–904CrossRef

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):360–367CrossRef

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):5–14CrossRef

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):717–727CrossRef

25.

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

25.

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

27.

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

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):1201–1215CrossRef

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

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

Title: Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs
Authors: Ajith Abraham
Crina Grosan
Ştefan Ţigan
Publication date: 01-05-2007
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 3/2007
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0090-1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 3/2007

Using evolution to improve neural network learning: pitfalls and solutions

An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

Hybrid artificial neural network

Reconfigurable hardware for neural networks: binary versus stochastic

Learning with partly labeled data

A data reduction approach for resolving the imbalanced data issue in functional genomics

Premium Partner