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An extension of the pharmacophore kernel using radial atomtype fingerprints

An extension of the pharmacophore kernel using radial atomtype fingerprints Chemistry Central Journal Open Access Poster presentation An extension of the pharmacophore kernel using radial atomtype fingerprints G Hinselmann*, M Eckert, T Holder, A Jahn, N Fechner and A Zell Address: University of Tübingen, Sand 1, 72076 Tübingen, Germany * Corresponding author from 4th German Conference on Chemoinformatics Goslar, Germany. 9–11 November 2008 Published: 5 June 2009 Chemistry Central Journal 2009, 3(Suppl 1):P11 doi:10.1186/1752-153X-3-S1-P11 <supplement> <title> <p>4th German Conference on Chemoinformatics: 22. CIC-Workshop</p> </title> <editor>Frank Oellien</editor> <note>Meeting abstracts – A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1752-153X-3-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1752-153X-3-S1-info.pdf</url> </supplement> This abstract is available from: http://www.journal.chemistrycentral.com/content/3/S1/P11 © 2009 Hinselmann et al; licensee BioMed Central Ltd. The prediction of the biological activity of a chemical onX 1.4 on various QSAR data sets taken from the litera- compound is a challenging task in Computational Chem- ture. The models were trained using the machine learning istry and was restricted to vectorial representations of the library LIBSVM. The results show that our approach molecular graph for decades. Kernel functions are positive improves the predictive power significantly on many semidefinite similarity measures that can be defined on benchmark problems. arbitrary structured data. This class of similarity functions can be used http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Chemistry Central Journal Springer Journals

An extension of the pharmacophore kernel using radial atomtype fingerprints

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An extension of the pharmacophore kernel using radial atomtype fingerprints

Abstract

Chemistry Central Journal Open Access Poster presentation An extension of the pharmacophore kernel using radial atomtype fingerprints G Hinselmann*, M Eckert, T Holder, A Jahn, N Fechner and A Zell Address: University of Tübingen, Sand 1, 72076 Tübingen, Germany * Corresponding author from 4th German Conference on Chemoinformatics Goslar, Germany. 9–11 November 2008 Published: 5 June 2009 Chemistry Central Journal 2009, 3(Suppl 1):P11 doi:10.1186/1752-153X-3-S1-P11...
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References (5)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Hinselmann et al; licensee BioMed Central Ltd.
Subject
Chemistry; Chemistry/Food Science, general
eISSN
1752-153X
DOI
10.1186/1752-153X-3-S1-P11
Publisher site
See Article on Publisher Site

Abstract

Chemistry Central Journal Open Access Poster presentation An extension of the pharmacophore kernel using radial atomtype fingerprints G Hinselmann*, M Eckert, T Holder, A Jahn, N Fechner and A Zell Address: University of Tübingen, Sand 1, 72076 Tübingen, Germany * Corresponding author from 4th German Conference on Chemoinformatics Goslar, Germany. 9–11 November 2008 Published: 5 June 2009 Chemistry Central Journal 2009, 3(Suppl 1):P11 doi:10.1186/1752-153X-3-S1-P11 <supplement> <title> <p>4th German Conference on Chemoinformatics: 22. CIC-Workshop</p> </title> <editor>Frank Oellien</editor> <note>Meeting abstracts – A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1752-153X-3-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1752-153X-3-S1-info.pdf</url> </supplement> This abstract is available from: http://www.journal.chemistrycentral.com/content/3/S1/P11 © 2009 Hinselmann et al; licensee BioMed Central Ltd. The prediction of the biological activity of a chemical onX 1.4 on various QSAR data sets taken from the litera- compound is a challenging task in Computational Chem- ture. The models were trained using the machine learning istry and was restricted to vectorial representations of the library LIBSVM. The results show that our approach molecular graph for decades. Kernel functions are positive improves the predictive power significantly on many semidefinite similarity measures that can be defined on benchmark problems. arbitrary structured data. This class of similarity functions can be used

Journal

Chemistry Central Journal – Springer Journals

Published: Jun 5, 2009

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