2005 Speical Issue: Graph kernels for chemical informatics

  • Authors:

  • Liva Ralaivola;Sanjay J. Swamidass;Hiroto Saigo;Pierre Baldi

  • Affiliations:

  • School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425, USA and Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697-3425, US ...;School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425, USA and Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697-3425, US ...;School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425, USA and Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697-3425, US ...;School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425, USA and Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697-3425, US ...

  • Venue:
  • Neural Networks - Special issue on neural networks and kernel methods for structured domains
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

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 depth-first 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.