Comparison of AdaBoost and genetic programming for combining neural networks for drug discovery.
In: Raidl, G and Cagnoni, S and Cardalda, JJR and Corne, DW and Gottlieb, J and Guillot, A and Hart, E and Johnson, CG and Marchiori, E and Meyer, JA and Middendorf, M, (eds.)
APPLICATIONS OF EVOLUTIONARY COMPUTING.
(pp. 87 - 98).
Genetic programming (GP) based data fusion and AdaBoost 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. Le. 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.
|Title:||Comparison of AdaBoost and genetic programming for combining neural networks for drug discovery|
|Event:||EvoWorkshops 2003 Conference|
|Location:||UNIV ESSEX, COLCHESTER, ENGLAND|
|Dates:||2003-04-14 - 2003-04-16|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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