Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds.
A novel method (in the context of quantitative structure-activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure-activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.
Asikainen
A H
AH
Department of Environmental Sciences, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland.
Ruuskanen
J
J
Tuppurainen
K A
KA
D000818:Animals; D019985:Benchmarking; D004785:Environmental Pollutants / Q000633:toxicity*; D004967:Estrogens / Q000494:pharmacology; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008956:Models, Chemical; D009930:Organic Chemicals / Q000633:toxicity; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000187:drug effects*
D004785:Environmental Pollutants; D004967:Estrogens; D009930:Organic Chemicals; D011960:Receptors, Estrogen