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.
Keywords: Analysis of variance, ANOVA, linear model, combinatorial chemistry
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