title: Fuzzy structure-activity relationships.
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.
authors.affiliation: SAIC-Frederick, Inc., Advanced Biomedical Computing Center, NCI Frederick, P.O. Box B, Frederick, MD 21702, USA. lukeb@ncifcrf.gov
authors.forename: B T
authors.initials: BT
authors.lastname: Luke
mesh_terms: D017143:Fuzzy Logic; D008956:Models, Chemical; D013329:Structure-Activity Relationship