Fuzzy structure-activity relationships |
| |
Authors: | BT Luke |
| |
Institution: | Advanced Biomedical Computing Center, NCI Frederick , SAIC-Frederick, Inc. , P.O. Box B, Frederick, MD, 21702, USA |
| |
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. |
| |
Keywords: | Fuzzy Classifiers Fuzzy Structure-activity Relationship Selwood Data K Nearest Neighbor (KNN) |
|
|