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Fuzzy structure-activity relationships

  1. Author:
    Luke, B. T.

  2. Author Address

    NCI, Adv Biomed Comp Ctr, SAIC Frederick Inc, POB B, Frederick, MD 21702 USA NCI, Adv Biomed Comp Ctr, SAIC Frederick Inc, Frederick, MD 21702 USA Luke BT NCI, Adv Biomed Comp Ctr, SAIC Frederick Inc, POB B, Frederick, MD 21702 USA
    1. Year: 2003
  2. Journal: SAR and QSAR in Environmental Research
    1. 14
    2. 1
    3. Pages: 41-57
  4. Type of Article: Article
  1. Abstract:

    While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure- activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset.

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