Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands

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.