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Article Dans Une Revue Journal of Chemical Information and Modeling Année : 2005

Graph kernels for molecular structure-activity relationship analysis with support vector machines.

Nobuhisa Ueda
  • Fonction : Auteur
Tatsuya Akutsu
  • Fonction : Auteur
Jean-Luc Perret
  • Fonction : Auteur
Jean-Philippe Vert

Résumé

The support vector machine algorithm together with graph kernel functions has recently been introduced to model structure-activity relationships (SAR) of molecules from their 2D structure, without the need for explicit molecular descriptor computation. We propose two extensions to this approach with the double goal to reduce the computational burden associated with the model and to enhance its predictive accuracy: description of the molecules by a Morgan index process and definition of a second-order Markov model for random walks on 2D structures. Experiments on two mutagenicity data sets validate the proposed extensions, making this approach a possible complementary alternative to other modeling strategies.

Dates et versions

hal-00433584 , version 1 (20-11-2009)

Identifiants

Citer

Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, Jean-Philippe Vert. Graph kernels for molecular structure-activity relationship analysis with support vector machines.. Journal of Chemical Information and Modeling, 2005, 45 (4), pp.939-51. ⟨10.1021/ci050039t⟩. ⟨hal-00433584⟩
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