Fast, Effective Molecular Feature Mining by Local Optimization

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In structure-activity-relationships (SAR) one aims at finding classifiers that predict the biological or chemical activity of a compound from its molecular graph. Many approaches to SAR use sets of binary substructure features, which test for the occurrence of certain substructures in the molecular graph. As an alternative to enumerating very large sets of frequent patterns, numerous pattern set mining and pattern set selection techniques have been proposed. Existing approaches can be broadly classified into those that focus on minimizing correspondences, that is, the number of pairs of training instances from different classes with identical encodings and those that focus on maximizing the number of equivalence classes, that is, unique encodings in the training data. In this paper we evaluate a number of techniques to investigate which criterion is a better indicator of predictive accuracy. We find that minimizing correspondences is a necessary but not sufficient condition for good predictive accuracy, that equivalence classes are a better indicator of success and that it is important to have a good match between training set and pattern set size. Based on these results we propose a new, improved algorithm which performs local minimization of correspondences, yet evaluates the effect of patterns on equivalence classes globally. Empirical experiments demonstrate its efficacy and its superior run time behavior.