Transductive Ridge Regression in Structure-activity Modeling.

Journal: Molecular informatics
Published Date:

Abstract

In this article we consider the application of the Transductive Ridge Regression (TRR) approach to structure-activity modeling. An original procedure of the TRR parameters optimization is suggested. Calculations performed on 3 different datasets involving two types of descriptors demonstrated that TRR outperforms its non-transductive analogue (Ridge Regression) in more than 90 % of cases. The most significant transductive effect was observed for small datasets. This suggests that transduction may be particularly useful when the data are expensive or difficult to collect.

Authors

  • Gilles Marcou
    Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg , 1 rue Blaise Pascal, Strasbourg 67000, France.
  • Grace Delouis
    Université de Strasbourg, Faculté de Chimie, 4 rue Blaise Pascal, BP 20296, 67008, Strasbourg Cedex, France.
  • Olena Mokshyna
    Université de Strasbourg, Faculté de Chimie, 4 rue Blaise Pascal, BP 20296, 67008, Strasbourg Cedex, France.
  • Dragos Horvath
    Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg , 1 rue Blaise Pascal, Strasbourg 67000, France.
  • Nicolas Lachiche
    ICube UMR 7357, 300 bd Sébastien Brant - CS 10413 -, F-67412, Illkirch Cedex.
  • Alexandre Varnek
    Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg , 1 rue Blaise Pascal, Strasbourg 67000, France.