Ridge regression ensemble for toxicity prediction

This paper proposes a purely data–driven, rigorous and universal methodology of QSAR modelling, based on ensemble of relatively simple ridge regressors trained in various subspaces of the chemical space, selected using an iterative optimization procedure.

Abstract

Traditional methods of assessing chemical toxicity of various compounds require tests on animals, which raises ethical concerns and is expensive. Current legislation may lead to a further increase of demand for laboratory animals in the next years. As a result, automatically generated predictions using Quantitative Structure–Activity Relationship (QSAR) modelling approaches appear as an attractive alternative. Due to sparsity of the chemical space, making this kind of predictions is however a difficult task. In this paper we propose a purely data–driven, rigorous and universal methodology of QSAR modelling, based on ensemble of relatively simple ridge regressors trained in various subspaces of the chemical space, selected using an iterative optimization procedure. The model described has been developed without using any domain knowledge and has been evaluated within the Environmental Toxicity Prediction Challenge CADASTER 2009, which has attracted over 100 participants from 25 countries. The presented approach was chosen as one of the First–Pass Winners, with predictive power non-significantly different to the highest ranked method, developed by the experts in the area of QSAR modelling and toxicology.