Comparison of adaboost and genetic programming for combining neural networks for drug discovery

  • Authors:

  • W. B. Langdon;S. J. Barrett;B. F. Buxton

  • Affiliations:

  • Data Exploration Sciences, GlaxoSmithKline, Research and Development, Greenford, Middlesex, UK;Data Exploration Sciences, GlaxoSmithKline, Research and Development, Greenford, Middlesex, UK;Computer Science, University College, London, UK

  • Venue:
  • EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
  • Year:
  • 2003

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Abstract

Genetic programming (GP) based data fusion and Ada Boost can both improve in vitro prediction of Cytochrome P450 activity by combining artificial neural networks (ANN). Pharmaceutical drug design data provided by high throughput screening (HTS) is used to train many base ANN classifiers. In data mining (KDD) we must avoid over fitting. The ensembles do extrapolate from the training data to other unseen molecules. I.e. they predict inhibition of a P450 enzyme by compounds unlike the chemicals used to train them. Thus the models might provide in silico screens of virtual chemicals as well as physical ones from Glaxo SmithKline (GSK)'s cheminformatics database. The receiver operating characteristics (ROC) of boosted and evolved ensemble are given.