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