Grid computing for the estimation of toxicity: acute toxicity on fathead minnow (pimephales promelas)

Authors:
Uko Maran;Sulev Sild;Paolo Mazzatorta;Mosé Casalegno;Emilio Benfenati;Mathilde Romberg
Affiliations:
Department of Chemistry, University of Tartu, Tartu, Estonia;Department of Chemistry, University of Tartu, Tartu, Estonia;Chemical Food Safety Group, Dep. of Quality & Safety, Nestléé Research Center, Lausanne, Switzerland;Istituto di Ricerche Farmacologiche "Mario Negri", Milano, Italy;Istituto di Ricerche Farmacologiche "Mario Negri", Milano, Italy;School of Biomedical Sciences, University of Ulster, Northern Ireland
Venue:
GCCB'06 Proceedings of the 2006 international conference on Distributed, high-performance and grid computing in computational biology
Year:
2007

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Abstract

The computational estimation of toxicity is time-consuming and therefore needs support for distributed, high-performance and/or grid computing. The major technology behind the estimation of toxicity is quantitative structure activity relationship modelling. It is a complex procedure involving data gathering, preparation and analysis. The current paper describes the use of grid computing in the computational estimation of toxicity and provides a comparative study on the acute toxicity of fathead minnow (Pimephales promelas) comparing the heuristic multi-linear regression and artificial neural network approaches for quantitative structure activity relationship models.