  An important first step in computational SAR modeling is to transform the
compounds into a representation that can be processed by predictive modeling
techniques. This is typically a feature vector where each feature indicates the
presence or absence of a molecular fragment. While the traditional approach to
SAR modeling employed size restricted fingerprints derived from path fragments,
much research in recent years focussed on mining more complex graph based
fragments. Today, there seems to be a growing consensus in the data mining
community that these more expressive fragments should be more useful. We
question this consensus and show experimentally that fragments of low
complexity, i.e. sequences, perform better than equally large sets of more
complex ones, an effect we explain by pairwise correlation among fragments and
the ability of a fragment set to encode compounds from different classes
distinctly. The size restriction on these sets is based on ordering the
fragments by class-correlation scores. In addition, we also evaluate the
effects of using a significance value instead of a length restriction for path
fragments and find a significant reduction in the number of features with
little loss in performance.
