Publikation: Graph Kernels for Molecular Similarity
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2010
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Schneider, Gisbert
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Molecular Informatics. Wiley. 2010, 29(4), pp. 266-273. ISSN 1868-1743. eISSN 1868-1751. Available under: doi: 10.1002/minf.200900080
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Graph kernels, Molecular similarity, Machine learning, Structure graph
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RUPP, Matthias, Gisbert SCHNEIDER, 2010. Graph Kernels for Molecular Similarity. In: Molecular Informatics. Wiley. 2010, 29(4), pp. 266-273. ISSN 1868-1743. eISSN 1868-1751. Available under: doi: 10.1002/minf.200900080BibTex
@article{Rupp2010-04-12Graph-52230,
year={2010},
doi={10.1002/minf.200900080},
title={Graph Kernels for Molecular Similarity},
number={4},
volume={29},
issn={1868-1743},
journal={Molecular Informatics},
pages={266--273},
author={Rupp, Matthias and Schneider, Gisbert}
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<dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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