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Open Access Green as soon as Postprint is submitted to ZB.
Graph kernels for molecular similarity.
Mol. Inform. 29, 266-273 (2010)
Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Graph kernels; Molecular similarity; Machine learning; Structure graph
ISSN (print) / ISBN
1868-1743
e-ISSN
1868-1751
Journal
Molecular Informatics
Quellenangaben
Volume: 29,
Issue: 4,
Pages: 266-273
Publisher
Wiley
Publishing Place
Weinheim
Non-patent literature
Publications
Reviewing status
Peer reviewed