title: Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up.
abstract: This article describes the use of a combination of extended-connectivity fingerprints (ECFPs) and Laplacian-modified Bayesian analysis in a study of the inhibition of Escherichia coli dihydrofolate reductase. The McMaster High-Throughput Screening Lab at McMaster University proposed a competition to predict the hits in a separate test set of 50,000 compounds. Although the problem seemed best approached with 3D methods, the authors show that 2D methods offer surprisingly competitive results with a low computational cost.
authors.affiliation: SciTegic Inc., 9665 Chesapeake Drive number 401, San Diego, CA 92123, USA. drogers@scitegic.com
authors.forename: David
authors.initials: D
authors.lastname: Rogers
authors.forename: Robert D
authors.initials: RD
authors.lastname: Brown
authors.forename: Mathew
authors.initials: M
authors.lastname: Hahn
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D004926:Escherichia coli / Q000201:enzymology; D005493:Folic Acid Antagonists / Q000737:chemistry* / Q000378:metabolism*; D010449:Peptide Mapping; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D005493:Folic Acid Antagonists; D013762:Tetrahydrofolate Dehydrogenase