title: Using clustering techniques to improve hit selection in high-throughput screening.
abstract: A typical modern high-throughput screening (HTS) operation consists of testing thousands of chemical compounds to select active ones for future detailed examination. The authors describe 3 clustering techniques that can be used to improve the selection of active compounds (i.e., hits). They are designed to identify quality hits in the observed HTS measurements. The considered clustering techniques were first tested on simulated data and then applied to analyze the assay inhibiting Escherichia coli dihydrofo-late reductase produced at the HTS laboratory of McMaster University.
authors.affiliation: Laboratoire LaCIM, Université du Québec à Montréal, C.P. 8888, succursale Centre-Ville, Montréal, Québec, Canada. gagarin@lacim.uqam.ca
authors.forename: Andrei
authors.initials: A
authors.lastname: Gagarin
authors.forename: Vladimir
authors.initials: V
authors.lastname: Makarenkov
authors.forename: Pablo
authors.initials: P
authors.lastname: Zentilli
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D013329:Structure-Activity Relationship