@article{4601,
  title = {Graph Kernels for Chemical Informatics},
  journal = {Neural Networks},
  abstract = {Increased availability of large repositories of chemical compounds is creating new
  challenges and opportunities for the application of machine learning methods to
  problems in computational chemistry and chemical informatics. Because chemical
  compounds are often represented by the graph of their covalent bonds, machine
  learning methods in this domain must be capable of processing graphical structures
  with variable size. Here we first briefly review the literature on graph kernels and
  then introduce three new kernels (Tanimoto, MinMax, Hybrid) based on the idea
  of molecular fingerprints and counting labeled paths of depth up to d using depthfirst
  search from each possible vertex. The kernels are applied to three classification
  problems to predict mutagenicity, toxicity, and anti-cancer activity on three publicly
  available data sets. The kernels achieve performances at least comparable, and most
  often superior, to those previously reported in the literature reaching accuracies of
  91.5% on the Mutag dataset, 65-67% on the PTC (Predictive Toxicology Challenge)
  dataset, and 72% on the NCI (National Cancer Institute) dataset. Properties and
  tradeoffs of these kernels, as well as other proposed kernels that leverage 1D or 3D
  representations of molecules, are briefly discussed.},
  volume = {18},
  number = {8},
  pages = {1093-1110},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005},
  slug = {4601},
  author = {Ralaivola, L. and Swamidass, JS. and Saigo, H. and Baldi, P.}
}