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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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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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