SPREAD—exploiting chemical features that cause differential activity behavior

We present a novel generic method to better understand the divergent activities of molecules that often occur in orthogonal assays. The newly developed simple prediction of activity differences (SPREAD) method directly aims to model and understand the differences compounds exhibit when tested in two or more assays. By transforming the activity values from the assays into meta-categories (specifically defined for datasets under scrutiny), statistical models can be trained directly on the qualitative differences between assays. This contributes heavily toward a tangible understanding of molecular assay selectivity. Although ensembles of models could be used alternatively to predict compounds that score highly in one assay and low in another, the advantage of the SPREAD approach is that the chemical features influencing assay differences are parsed out immediately as a consequence of training the model on the coincident assay differences. By training the model that describes the difference between two or more assays, molecular substructures that are responsible for assay selectivity can be parsed out. The method was validated by using four challenging datasets. Copyright © 2009 Wiley Periodicals, Inc., Statistical Analysis and Data Mining 2: 115-122, 2009