Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity

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2007
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Proschak, Ewgenij
Schneider, Gisbert
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Journal of Chemical Information and Modeling. American Chemical Society (ACS). 2007, 47(6), pp. 2280-2286. ISSN 1549-9596. eISSN 1549-960X. Available under: doi: 10.1021/ci700274r
Zusammenfassung

Similarity measures for molecules are of basic importance in chemical, biological, and pharmaceutical applications. We introduce a molecular similarity measure defined directly on the annotated molecular graph, based on iterative graph similarity and optimal assignments. We give an iterative algorithm for the computation of the proposed molecular similarity measure, prove its convergence and the uniqueness of the solution, and provide an upper bound on the required number of iterations necessary to achieve a desired precision. Empirical evidence for the positive semidefiniteness of certain parametrizations of our function is presented. We evaluated our molecular similarity measure by using it as a kernel in support vector machine classification and regression applied to several pharmaceutical and toxicological data sets, with encouraging results.

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ISO 690RUPP, Matthias, Ewgenij PROSCHAK, Gisbert SCHNEIDER, 2007. Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity. In: Journal of Chemical Information and Modeling. American Chemical Society (ACS). 2007, 47(6), pp. 2280-2286. ISSN 1549-9596. eISSN 1549-960X. Available under: doi: 10.1021/ci700274r
BibTex
@article{Rupp2007Kerne-52197,
  year={2007},
  doi={10.1021/ci700274r},
  title={Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity},
  number={6},
  volume={47},
  issn={1549-9596},
  journal={Journal of Chemical Information and Modeling},
  pages={2280--2286},
  author={Rupp, Matthias and Proschak, Ewgenij and Schneider, Gisbert}
}
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