Drug Activity Characterization Using One-Class Support Vector Machines with Counterexamples

  • Alicia Hurtado-Cortegana
  • Francesc J. Ferri
  • Wladimiro Diaz-Villanueva
  • Carlos Morell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

The problem of detecting chemical activity in drugs from its molecular description constitutes a challenging and hard learning task. The corresponding prediction problem can be tackled either as a binary classification problem (active versus inactive compounds) or as a one class problem. The first option leads usually to better prediction results when measured over small and fixed databases while the second could potentially lead to a much better characterization of the active class which could be more important in more realistic settings. In this paper, a comparison of these two options is presented when support vector models are used as predictors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alicia Hurtado-Cortegana
    • 1
  • Francesc J. Ferri
    • 1
  • Wladimiro Diaz-Villanueva
    • 1
  • Carlos Morell
    • 2
  1. 1.Departament d’InformàticaUniversitat de ValènciaSpain
  2. 2.Comp. Sci. Dept.Univ. Central ”Marta Abreu” de Las VillasSanta ClaraCuba

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