PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating the domain of applicability for machine learning qsar rmodels: A study on aqueous solubility of drug discovery molecules
Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich and Klaus-Robert Müller
Journal of Computer Aided Molecular Design Volume 21, Number 9, pp. 485-498, 2007.

Abstract

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3353
Deposited By:Anton Schwaighofer
Deposited On:08 February 2008