title: The reduced graph descriptor in virtual screening and data-driven clustering of high-throughput screening data.
abstract: Virtual screening and high-throughput screening are two major components of lead discovery within the pharmaceutical industry. In this paper we describe improvements to previously published methods for similarity searching with reduced graphs, with a particular focus on ligand-based virtual screening, and describe a novel use of reduced graphs in the clustering of high-throughput screening data. Literature methods for reduced graph similarity searching encode the reduced graphs as binary fingerprints, which has a number of issues. In this paper we extend the definition of the reduced graph to include positively and negatively ionizable groups and introduce a new method for measuring the similarity of reduced graphs based on a weighted edit distance. Moving beyond simple similarity searching, we show how more flexible queries can be built using reduced graphs and describe a database system that allows iterative querying with multiple representations. Reduced graphs capture many important features of ligand-receptor interactions and, in conjunction with other whole molecule descriptors, provide an informative way to review HTS data. We describe a novel use of reduced graphs in this context, introducing a method we have termed data-driven clustering, that identifies clusters of molecules represented by a particular whole molecule descriptor and enriched in active compounds.
authors.affiliation: GlaxoSmithKline, Gunnels Wood Road, Stevenage SG1 2NY, United Kingdom. gavin.x.harper@gsk.com
authors.forename: G
authors.initials: G
authors.lastname: Harper
authors.forename: G S
authors.initials: GS
authors.lastname: Bravi
authors.forename: S D
authors.initials: SD
authors.lastname: Pickett
authors.forename: J
authors.initials: J
authors.lastname: Hussain
authors.forename: D V S
authors.initials: DV
authors.lastname: Green