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Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development

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Applications of Soft Computing

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 36))

Abstract

Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. They are inherently suitable for use with ‘noisy’, high dimensional (many variables) data, as is commonly used in cheminformatic (i.e. In silico screening), bioinformatic (i.e. bio-marker studies, using DNA chip data) and other types of drug research studies. These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities.

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Authors and Affiliations

  1. Analysis Applications, Research and Technologies, GlaxoSmithKline R&D, Greenford Rd, Greenford, Middlesex, UB6 OHE, UK

    S. J. Barrett

  2. Computer Science, University of Essex, Colchester, C04 3SQ, UK

    W. B. Langdon

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Editors and Affiliations

  1. Enterprise Integration School of Industrial and Manufacturing Science (SIMS), Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK

    Ashutosh Tiwari  & Rajkumar Roy  & 

  2. School of Chemistry, University of Manchester, Faraday Building, Sackville Street, PO Box 88, Manchester, M6o 1QD, UK

    Joshua Knowles

  3. Centre for Transport & Society Faculty of the Built Environment, University of the West of England, Frenchay Campus Coldharbour Lane, Bristol, BS16 1QY, UK

    Erel Avineri

  4. School of Informatics, University of Bradford, Richmond Road, Bradford, West Yorkshire, BD7 1DP, UK

    Keshav Dahal

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Barrett, S.J., Langdon, W.B. (2006). Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development. In: Tiwari, A., Roy, R., Knowles, J., Avineri, E., Dahal, K. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36266-1_10

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