Data Mining Algorithms for Virtual Screening of Bioactive Compounds

@inproceedings{Deshpande2007DataMA,
  title={Data Mining Algorithms for Virtual Screening of Bioactive Compounds},
  author={M. Deshpande and M. Kuramochi and G. Karypis},
  year={2007}
}
  • M. Deshpande, M. Kuramochi, G. Karypis
  • Published 2007
  • Computer Science
  • 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… CONTINUE READING
    2 Citations

    Topics from this paper

    References

    SHOWING 1-10 OF 74 REFERENCES
    Comparisons of classification methods for screening potential compounds
    • Aijun An, Y. Wang
    • Computer Science
    • Proceedings 2001 IEEE International Conference on Data Mining
    • 2001
    • 13
    Automated Approaches for Classifying Structures
    • 65
    • PDF
    Data analysis of high-throughput screening results: application of multidomain clustering to the NCI anti-HIV data set.
    • 31
    Mining molecular fragments: finding relevant substructures of molecules
    • C. Borgelt, M. Berthold
    • Computer Science
    • 2002 IEEE International Conference on Data Mining, 2002. Proceedings.
    • 2002
    • 485
    • PDF
    Finding Frequent Substructures in Chemical Compounds
    • 312
    • PDF
    Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning
    • 129
    Analysis of Large Screening Data Sets via Adaptively Grown Phylogenetic-Like Trees
    • 42
    Molecular feature mining in HIV data
    • 265
    • PDF
    Warmr: a data mining tool for chemical data
    • 80