Structure-activity similarity maps

Activity cliff, Structure-activity similarity maps, Structure-activity landscape index, Structure-activity... 

Structure-activity similarity (SAS) maps, first described by Shanmugasundaram and Maggiora (35), are pairwise plots of the structure similarity against the activity similarity. The resultant plot can be divided into four quadrants, allowing one to identify molecules characteristic of one of four possible behaviors smooth regions of the SAR space (rough), activity cliffs, nondescript (i.e., low structural similarity and low activity similarity), and scaffold hops (low structural similarity but high activity similarity). Recently, SAS maps have been extended to take into account multiple descriptor representations (two and three dimensions) (36, 37). In addition to SAS maps, other pairwise metrics to characterize and visualize SAR landscapes have been developed such as the structure-activity landscape index (SALI) (38) and the structure-activity index (SARI) (39). 

A particularly useful way toward a graphical representation of chemical similarity versus biological similarity was introduced using the so-called structure-activity similarity (SAS) maps [104], These maps provide a graphical and numerical tool for the analysis of SAR on their x-axis, the chemical similarity of compound pairs is plotted versus the biological similarity on the y-axis. Based on the pairwise comparison of chanical structure and biological activity in a compound series, regions with different SAR characteristics can be identified. 

As will be discussed in more detail in Section 15.6.3, structure-activity similarity (S AS) maps also afford a means not only for characterizing activity cliffs but also the entire activity landscapes within which they are embedded. 

Once a lead is found, we have to focus in our search of property space as rapidly as possible to understand structure-activity relationships and perhaps build pharmacophore maps. To do this, we need to design more compounds to explore ranges of properties centered around our lead. This explosion of compounds is a knowledge-gathering process which, when the compounds are tested, should increase the understanding of bioactivity. By application of the neighborhood principle to eliminate similar molecules, any molecules selected for synthesis will likely have several hundred or a thousand similar structures that are stored in a virtual library. These often represent an ideal starting point for lead follow-up. 

The next step was made by Klebe et al. [50]. Two 3D-QSAR methods were applied to get three-dimensional quantitative structure-activity relationships using a training set of 72 inhibitors of the benzamidine type with respect to their binding affinities toward Factor Xa to yield statistically reliable models of good predictive power [51-54] the widely used CoMFA method (for steric and electrostatic properties) and the comparative molecular similarity index analysis (CoMSlA) method (for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties). These methods allowed the consideration of various physicochemical properties, and the resulting contribution maps could be intuitively interpreted. 

Medina-Franco JL, Yongye AB, Perez-VUlanneva J, et al. Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. J Chem Inf Model 2011 51 2427-2439. 

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