Corpus ID: 5159247

Finding Frequent Substructures in Chemical Compounds

@inproceedings{Dehaspe1998FindingFS,
  title={Finding Frequent Substructures in Chemical Compounds},
  author={L. Dehaspe and Hannu Toivonen and R. King},
  booktitle={KDD},
  year={1998}
}
  • L. Dehaspe, Hannu (TT) Toivonen, R. King
  • Published in KDD 1998
  • Computer Science
    • The discovery of the relationships between chemical structure and biological function is central to biological science and medicine. In this paper we apply data mining to the problem of predicting chemical carcinogenicity. This toxicology application was launched at IJCAI'97 as a research challenge for artificial intelligence. Our approach to the problem is descriptive rather than based on classification; the goal being to find common substructures and properties in chemical compounds, and in… CONTINUE READING
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    References

    SHOWING 1-10 OF 50 REFERENCES
    Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail
    • 13
    • PDF
    Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming.
    • 72
    • PDF
    Carcinogenesis Predictions Using ILP
    • 146
    Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.
    • 257
    • PDF
    Frequent query discovery: a unifying ILP approach to association rule mining
    • 15
    • PDF
    The Predictive Toxicology Evaluation Challenge
    • 137
    • PDF
    Analyzing the Benefits of Domain Knowledge in Substructure Discovery
    • 33
    • PDF
    Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP.
    • 656
    Metaqueries for Data Mining
    • 114
    STRUCTURE—ACTIVITY RELATIONSHIPS
    • W. A. Sexton
    • Chemistry, Medicine
    • The Journal of pharmacy and pharmacology
    • 1958
    • 375