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Conference Paper

A Linear Programming Approach for Molecular QSAR analysis

MPS-Authors
/persons/resource/persons84183

Saigo,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://kops.uni-konstanz.de/bitstream/handle/123456789/5412/MLG2006_Proceedings.pdf
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MLG-2006-Saigo.pdf
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Citation

Saigo, H., Kadowaki, T., & Tsuda, K. (2009). A Linear Programming Approach for Molecular QSAR analysis. In T. Gärtner, G. Garriga, & T. Meinl (Eds.), MLG 2006: Proceedings of the International Workshop on Mining and Learning with Graphs in conjunction with ECML/PKDD 2006 (pp. 85-96). Konstanz, Germany: Bibliothek der Universität Konstanz.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D03B-6
Abstract
Small molecules in chemistry can be represented as graphs.
In a quantitative structure-activity relationship (QSAR) analysis, the
central task is to find a regression function that predicts
the activity of the molecule in high accuracy.
Setting a QSAR as a primal target, we propose a new linear
programming approach to the graph-based regression problem.
Our method extends the graph classification algorithm by Kudo et al.
(NIPS 2004), which is a combination of boosting and graph mining.
Instead of sequential multiplicative updates, we employ the linear
programming boosting (LP) for regression. The LP approach allows to
include inequality constraints for the parameter vector, which turns out to
be particularly useful in QSAR tasks where activity values are
sometimes unavailable.
Furthermore, the efficiency is improved significantly by employing
multiple pricing.