Many bioinformatics applications utilize machine learning techniques to
create models for predicting which parts of proteins will bind to targets.
Understanding the results of these protein surface binding classifiers is
challenging, as the individual answers are embedded spatially on the surface
of the molecules, yet the performance needs to be understood over an entire
corpus of molecules. In this project, we introduce a multi-scale approach for
assessing the performance of these structural classifiers, providing
coordinated views for both corpus level overviews as well as
spatially-embedded results on the three-dimensional structures of proteins.