Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs

A new method for analyzing a structure−activity relationship is proposed. By use of a simple quantitative
index, one can readily identify “structure−activity cliffs”:  pairs of molecules which are most similar but
have the largest change in activity. We show how this provides a graphical representation of the entire
SAR, in a way that allows the salient features of the SAR to be quickly grasped. In addition, the approach
allows us view the SARs in a data set at different levels of detail. The method is tested on two data sets that
highlight its ability to easily extract SAR information. Finally, we demonstrate that this method is robust
using a variety of computational control experiments and discuss possible applications of this technique to
QSAR model evaluation.