The Institute of Pharmaceutical Innovation

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Dr Qun Shao
China Programme Manager
  • Telephone: +44 (0) 1274 236041
  • Email: q.shao@Bradford.ac.uk

Dr. Qun Shao holds a PhD degree in Data Mining for Product Formulation and Processing. His role at the IPI involves identifying and managing artificial intelligence computational research projects (with the principal application to pharmaceuticals) and providing expertise in data mining and analysis with artificial intelligence tools as part of the academic team. Dr. Shao also takes the lead role in identifying, initiating and consolidating academic and industrial pharmaceutical research collaborations in China.

Current research interest

  • Investigation and establishment of appropriate strategies for modelling and data-mining pharmaceutical data with various characteristics from wide application domains (e.g. formulation design and process optimisation, in-vitro in-vivo correlation) using existing artificial intelligence (AI) tools.

  • Development of data mining methodologies and tools based on artificial intelligence technologies for the application to the modernisation of traditional Chinese Medicine (TCM).

  • Evaluation of broad range of knowledge engineering and artificial intelligence technologies as applied to pharmaceutical application.

    • Key publications

    [1] Shao, Q., Rowe, R.C. and York, P. (2007). Data mining of fractured experimental data using neurofuzzy logic – discovering and integrating knowledge hidden in multiple formulation databases for a fluid-bed granulation process. J. Pharm. Sci. (Accepted)

    [2] Shao, Q., Rowe, R.C. and York, P. (2007). Comparison of decision trees and neurofuzzy logic in discovering knowledge from experimental data of an immediate release tablet formulation. Eur. J. Pharm. Sci., 31, 129-136.

    [3] Shao, Q., Rowe, R.C. and York, P. (2007). Investigation of an artificial intelligence technology -model trees: novel application for an immediate release tablet formulation database. Eur. J. Pharm. Sci., 32, 137-144.

    [4] Matas, M.D.; Shao Q.; Silkstone, V. L.; Chrystyn, H. (2007). Evaluation of an in-vitro in-vivo correlation for nebulizer delivery using artificial neural networks. J. Pharm. Sci., DOI 10.1002/jps.20965.

    [5] Matas, M.D.; Shao Q.; Shukla, R. (2007). Artificial Intelligence – the key to process understanding. Pharm. Tech. Eu., January, 44-48.

    [6] Shao, Q., Rowe, R.C. and York, P. (2006). Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eur. J. Pharm. Sci., 28, 394-404.