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A Linear Programming Approach for Molecular QSAR analysis

2006

Conference Paper

ei


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.

Author(s): Saigo, H. and Kadowaki, T. and Tsuda, K.
Book Title: MLG 2006
Journal: Proceedings of the International Workshop on Mining and Learning with Graphs 2006 (MLG 2006)
Pages: 85-96
Year: 2006
Month: September
Day: 0
Editors: G{\"a}rtner, T. , G. C. Garriga, T. Meinl

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: International Workshop on Mining and Learning with Graphs 2006
Event Place: Berlin, Germany

Digital: 0
Language: en
Note: Best Paper Award
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{4160,
  title = {A Linear Programming Approach for Molecular QSAR analysis},
  author = {Saigo, H. and Kadowaki, T. and Tsuda, K.},
  journal = {Proceedings of the International Workshop on Mining and Learning with Graphs 2006 (MLG 2006)},
  booktitle = {MLG 2006},
  pages = {85-96},
  editors = {G{\"a}rtner, T. , G. C. Garriga, T. Meinl},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2006},
  note = {Best Paper Award},
  month_numeric = {9}
}