Neural Computing and Applications

, Volume 16, Issue 3, pp 307–316 | Cite as

Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs

Original Article

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.

Keywords

Hybrid learning Ensemble learning Evolutionary neural network Neuro-fuzzy Drug design 

Notes

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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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Faculty of Information Technology, Mathematics and Electrical EngineeringNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Department of Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania
  3. 3.Faculty of Medicine, Department of Biostatistics and Medical InformaticsUniversity Iuliu HaţieganuCluj-NapocaRomania

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