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. © Springer-Verlag Berlin Heidelberg 2007.
Grid computing for the estimation of toxicity: Acute toxicity on Fathead Minnow (Pimephales promelas)
Casalegno M.;
2007-01-01
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. © Springer-Verlag Berlin Heidelberg 2007.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
