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Rupp, M. ; Schneider, G.*

Graph kernels for molecular similarity.

Mol. Inform. 29, 266-273 (2010)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Graph kernels; Molecular similarity; Machine learning; Structure graph
ISSN (print) / ISBN 1868-1743
e-ISSN 1868-1751
Zeitschrift Molecular Informatics
Quellenangaben Band: 29, Heft: 4, Seiten: 266-273 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort Weinheim
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)