PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classifying 'drug-likeness' with kernel-based learning methods
Klaus-Robert Müller, Gunnar Raetsch, Sören Sonnenburg, Sebastian Mika, Michael Grimm and Nikolaus Heinrich
J. Chem. Inf. Model Volume 45, Number 2, pp. 249-253, 2005.

Abstract

In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1091
Deposited By:Sören Sonnenburg
Deposited On:19 September 2005