Literature DB >> 27464016

Estimation of Acid Dissociation Constants Using Graph Kernels.

Matthias Rupp1, Robert Körner2, Igor V Tetko2.   

Abstract

The biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups. We estimate these constants using kernel ridge regression and graph kernels. The performance of our approach is similar to that of a semi-empirical model (Tehan et al, QSAR & Comb. Sci. 21(5): 457-472, 473-485) based on frontier electron theory, but uses only the annotated structure graph. In particular, no structure optimization is necessary. We discuss advantages and shortcomings of our approach.
Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  Acid dissociation constant; Graph kernel; Kernel ridge regression; QSPR; pKa

Year:  2010        PMID: 27464016     DOI: 10.1002/minf.201000072

Source DB:  PubMed          Journal:  Mol Inform        ISSN: 1868-1743            Impact factor:   3.353


  1 in total

1.  Multi-task learning for pKa prediction.

Authors:  Grigorios Skolidis; Katja Hansen; Guido Sanguinetti; Matthias Rupp
Journal:  J Comput Aided Mol Des       Date:  2012-06-20       Impact factor: 3.686

  1 in total

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