Grid Computing for the Estimation of Toxicity: Acute Toxicity on Fathead Minnow (Pimephales promelas)

  • Uko Maran
  • Sulev Sild
  • Paolo Mazzatorta
  • Mos Casalegno
  • Emilio Benfenati
  • Mathilde Romberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4360)

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.

Keywords

modelling and prediction chemistry QSAR molecular descriptors workflow distributed computing 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Uko Maran
    • 1
  • Sulev Sild
    • 1
  • Paolo Mazzatorta
    • 2
  • Mos Casalegno
    • 3
  • Emilio Benfenati
    • 3
  • Mathilde Romberg
    • 4
  1. 1.Department of Chemistry, University of Tartu, Jakobi 2, Tartu 51014Estonia
  2. 2.Chemical Food Safety Group, Dep. of Quality & Safety, Nestlé Research Center, P.O. Box 44, CH-10000 Lausanne 26Switzerland
  3. 3.Istituto di Ricerche Farmacologiche “Mario Negri”, Via Eritrea 62, 20157 MilanoItaly
  4. 4.School of Biomedical Sciences, University of Ulster, Cromore Road, Coleraine BT52 1SANorthern Ireland

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