PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Linear Programming Approach for Molecular QSAR analysis
Hiroto Saigo, Tadashi Kadowaki and Koji Tsuda
In: International Workshop on Mining and Learning with Graphs 2006, 18/09/2006, Berlin.

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.

EPrint Type:Conference or Workshop Item (Talk)
Additional Information:Best Paper Award
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3108
Deposited By:Hiroto Saigo
Deposited On:19 December 2007