The use of high throughput screening (HTS) to identify lead compounds has g
reatly challenged conventional quantitative structure-activity relationship
(QSAR) techniques that typically correlate structural variations in simila
r compounds with continuous changes in biological activity. A new QSAR-like
methodology that can correlate less quantitative assay data (i.e., "active
" versus "inactive"), as initially generated by HTS, has been introduced. I
n the present study, we have, for the first time, applied this approach to
a drug discovery problem; that is, the study of estrogen receptor ligands.
The binding affinities of 463 estrogen analogues were transformed into a bi
nary data format, and a predictive binary QSAR model was derived using 410
estrogen analogues as a training set. The model was applied to predict the
activity of 53 estrogen analogues not included in the training set. An over
all accuracy of 94% was obtained.