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A Linear Programming Approach for Molecular QSAR analysis

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84183

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

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

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Zitation

Saigo, H., Kadowaki, T., & Tsuda, K. (2006). A Linear Programming Approach for Molecular QSAR analysis. Proceedings of the International Workshop on Mining and Learning with Graphs 2006 (MLG 2006), 85-96.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D03B-6
Zusammenfassung
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.