title: Graph Kernels for Molecular Similarity.
abstract: 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.
authors.affiliation: Beilstein Endowed Chair for Cheminformatics, Goethe University, Siesmayerstr. 70, 60323 Frankfurt am Main, Germany. mrupp@mrupp.info.|Present Address: Institute for Bioinformatics and Systems Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. mrupp@mrupp.info.
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.affiliation: Institute of Pharmaceutical Sciences, Eidgenössische Technische Hochschule (ETH) Zürich, Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland.
authors.forename: Gisbert
authors.initials: G
authors.lastname: Schneider
keywords: Graph kernels; Machine learning; Molecular similarity; Structure graph