title: Classifying 'drug-likeness' with kernel-based learning methods.
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.
authors.affiliation: Fraunhofer FIRST, Kekuléstrasse 7, 12489 Berlin, Germany.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Gunnar
authors.initials: G
authors.lastname: Rätsch
authors.forename: Sören
authors.initials: S
authors.lastname: Sonnenburg
authors.forename: Sebastian
authors.initials: S
authors.lastname: Mika
authors.forename: Michael
authors.initials: M
authors.lastname: Grimm
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Heinrich
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D015195:Drug Design; D008956:Models, Chemical