title: Analysis and prediction of combinatorial chemistry synthesis and screening data.
abstract: The goal of combinatorial chemistry is to simultaneously synthesize sets of compounds possessing properties that are then distinguished through screening. As the size of a compound set increases, data analysis becomes more challenging. Analysis of Variance (ANOVA) is an accepted statistical method that offers a straightforward solution to this problem. Two steps encountered by combinatorial scientists appear well suited to ANOVA: the prediction of synthetic outcomes (purity and yield) of set members and the analysis of screening data to identify combinations of reagent inputs that result in molecules with a desired property. To illustrate, a subset of a combinatorial array, referred to as a reaction rehearsal set, is evaluated to create a model predictive of the individual synthetic outcomes of the full matrix. In a second exercise, the biochemical screening data obtained from a combinatorial library is analyzed to identify reagent interactions that result in molecules possessing the sought activity.
authors.affiliation: Daiichi-Sankyo, Inc., Edison, NJ 08837, USA. nge@daiichisankyo-us.com
authors.forename: Nanxiang
authors.initials: N
authors.lastname: Ge
authors.forename: Paul
authors.initials: P
authors.lastname: Fogel
authors.forename: Sidney S
authors.initials: SS
authors.lastname: Young
authors.forename: Richard E
authors.initials: RE
authors.lastname: Austin
authors.forename: Eric
authors.initials: E
authors.lastname: Wegrzyniak
authors.forename: James A
authors.initials: JA
authors.lastname: Connelly
mesh_terms: D000704:Analysis of Variance; D020650:Combinatorial Chemistry Techniques; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data*; D016014:Linear Models; D008956:Models, Chemical; D011237:Predictive Value of Tests