Data Mining Algorithms for Virtual Screening of Bioactive Compounds

Mukund Deshpande (), Michihiro Kuramochi () and George Karypis ()
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Mukund Deshpande: University of Minnesota
Michihiro Kuramochi: University of Minnesota
George Karypis: University of Minnesota

A chapter in Data Mining in Biomedicine, 2007, pp 59-90 from Springer

Abstract: Abstract In this chapter 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 this 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. Experimental evaluation on eight different classification problems shows that our approach is computationally scalable and on the average, outperforms existing schemes by 10% to 35%.

Keywords: Classification; Chemical Compounds; Virtual Screening; Graphs; SVM (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_5

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