English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Graph Kernels for Chemical Informatics

Ralaivola, L., Swamidass, J., Saigo, H., & Baldi, P. (2005). Graph Kernels for Chemical Informatics. Neural networks, 18(8), 1093-1110. doi:10.1016/j.neunet.2005.07.009.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Ralaivola, L, Author
Swamidass , JS, Author
Saigo, H1, Author              
Baldi, P, Author
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2005-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neunet.2005.07.009
BibTex Citekey: 4601
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Neural networks
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 18 (8) Sequence Number: - Start / End Page: 1093 - 1110 Identifier: ISSN: 0893-6080
CoNE: https://pure.mpg.de/cone/journals/resource/954925558496