# Item

ITEM ACTIONSEXPORT

Released

Conference Paper

#### A Linear Programming Approach for Molecular QSAR analysis

##### MPS-Authors

##### Locator

There are no locators available

##### Fulltext (public)

There are no public fulltexts available

##### Supplementary Material (public)

There is no public supplementary material available

##### Citation

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.

Cite as: http://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.