Fuzzy structure-activity relationships

被引：0
|
作者：
Luke, BT [1 ]
机构：
[1] NCI, Adv Biomed Comp Ctr, SAIC Frederick Inc, Frederick, MD 21702 USA
来源：
SAR AND QSAR IN ENVIRONMENTAL RESEARCH | 2003年 / 14卷 / 01期
关键词：
Fuzzy classifiers; Fuzzy structure-activity relationship; Selwood data; K nearest neighbor (KNN);
D O I：
10.1080/1062936021000058773
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
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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页码：41 / 57
页数：17
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