PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Graph kernels for molecular structure-activity relationship analysis with support vector machines
Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret and Jean-Philippe Vert
Journal of Chemical Information and Modeling Volume 45, Number 4, pp. 939-951, 2005.

Abstract

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:1400
Deposited By:Jean-Philippe Vert
Deposited On:28 November 2005