title: Active learning with support vector machines in the drug discovery process.
abstract: We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparatively small batch of compounds is screened for binding activity toward this target. We employed the so-called "active learning paradigm" from Machine Learning for selecting the successive batches. Our main selection strategy is based on the maximum margin hyperplane-generated by "Support Vector Machines". This hyperplane separates the current set of active from the inactive compounds and has the largest possible distance from any labeled compound. We perform a thorough comparative study of various other selection strategies on data sets provided by DuPont Pharmaceuticals and show that the strategies based on the maximum margin hyperplane clearly outperform the simpler ones.
authors.affiliation: Computer Science Department, University of California, Santa Cruz, California 95064, USA. manfred@cse.ucsc.edu
authors.forename: Manfred K
authors.initials: MK
authors.lastname: Warmuth
authors.forename: Jun
authors.initials: J
authors.lastname: Liao
authors.forename: Gunnar
authors.initials: G
authors.lastname: Rätsch
authors.forename: Michael
authors.initials: M
authors.lastname: Mathieson
authors.forename: Santosh
authors.initials: S
authors.lastname: Putta
authors.forename: Christian
authors.initials: C
authors.lastname: Lemmen
mesh_terms: D017076:Computer-Aided Design; D015195:Drug Design