Graph kernels based on tree patterns for molecules
type: Dataset
description: Motivated by chemical applications, we revisit and extend a family of positive definite kernels for graphs based on the detection of common subtrees, initially proposed by Ramon et al. (2003). We propose new kernels with a parameter to control the complexity of the subtrees used as features to represent the graphs. This parameter allows to smoothly interpolate between classical graph kernels based on the count of common walks, on the one hand, and kernels that emphasize the detection of large common subtrees, on the other hand. We also propose two modular extensions to this formulation. The first extension increases the number of subtrees that define the feature space, and the second one removes noisy features from the graph representations. We validate experimentally these new kernels on binary classification tasks consisting in discriminating toxic and non-toxic molecules with support vector machines.
landing page: https://hal.science/hal-00095488
keyword: chemoinformatics
keyword: chimchemchemical-sciencescheminformatics
keyword: infoeu-reposemanticspreprint
keyword: kernels
keyword: preprints-working-papers-
keyword: sdvbibslife-sciences-q-bioquantitative-methods-q-bioqm
keyword: support-vector-machines
issued: 2026-05-06T04:02:30.353693
modified: 2026-05-06T04:02:30.353698
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Graph kernels based on tree patterns for molecules
type: Distribution
format: HTML
access URL: https://hal.science/hal-00095488
issued: 2026-05-06T04:02:30.357524
modified: 2026-05-06T04:02:30.342919