title: Graph kernels for molecular structure-activity relationship analysis with support vector machines.
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.
authors.affiliation: Ecole des Mines de Paris, 35 rue Saint Honoré, 77305 Fontainebleau, France. pierre.mahe@ensmp.fr
authors.forename: Pierre
authors.initials: P
authors.lastname: Mahé
authors.forename: Nobuhisa
authors.initials: N
authors.lastname: Ueda
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Perret
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Vert
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D008390:Markov Chains; D008962:Models, Theoretical; D009152:Mutagenicity Tests; D013329:Structure-Activity Relationship