title: Comparison of ranking methods for virtual screening in lead-discovery programs.
abstract: This paper discusses the use of several rank-based virtual screening methods for prioritizing compounds in lead-discovery programs, given a training set for which both structural and bioactivity data are available. Structures from the NCI AIDS data set and from the Syngenta corporate database were represented by two types of fragment bit-string and by sets of high-level molecular features. These representations were processed using binary kernel discrimination, similarity searching, substructural analysis, support vector machine, and trend vector analysis, with the effectiveness of the methods being judged by the extent to which active test set molecules were clustered toward the top of the resultant rankings. The binary kernel discrimination approach yielded consistently superior rankings and would appear to have considerable potential for chemical screening applications.
authors.affiliation: Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Sheffield S10 2TN, UK. d.j.wilton@sheffield.ac.uk
authors.forename: David
authors.initials: D
authors.lastname: Wilton
authors.forename: Peter
authors.initials: P
authors.lastname: Willett
authors.forename: Kevin
authors.initials: K
authors.lastname: Lawson
authors.forename: Graham
authors.initials: G
authors.lastname: Mullier