Frequent Sub-structure Based Approaches for Classifying Chemical Compounds

Mukund Deshpande, Michihiro Kuramochi, and George Karypis
3nd IEEE Conference on Data Mining (ICDM), pp. 35-42, 2003
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In this paper we study the problem of classifying chemical compound datasets. We present a sub-structure-based classification algorithm that decouples the sub-structure discovery process from the classification model construction and uses frequent subgraph discovery algorithms to find all topological and geometric sub-structures present in the dataset. The advantage of our approach is that during classification model construction, all relevant sub-structures are available allowing the classifier to intelligently select the most discriminating ones. The computational scalability is ensured by the use of highly efficient frequent subgraph discovery algorithms coupled with aggressive feature selection. Our experimental evaluation on eight different classification problems shows that our approach is computationally scalable and outperforms existing schemes by 10% to 35%, on the average.
This is an expanded version of the BIOKDD02 paper.
Research topics: Bioinformatics | Cheminformatics | Classification | Data mining