Identification of multitarget activity ridges in high-dimensional bioactivity spaces.

An activity cliff is defined as a pair of structurally similar compounds that have a large difference in potency against a given target. The activity cliff concept has recently been extended in different ways, including the introduction of the activity ridge data structure. An activity ridge consists of two subsets of highly and weakly potent structurally analogous compounds that form all possible pairwise activity cliffs between them. As such, the activity ridge data structure is rich in structure-activity relationship (SAR) information and attractive for SAR analysis. Activity ridges have been detected in various compound data sets. Analogously to single-target activity cliffs, activity rides have thus far only been investigated for individual targets. In this study, we have asked the question whether multitarget activity ridges might also exist. The analysis has been complicated by the limited availability of suitable compound profiling data sets in the public domain. However, in a high-dimensional kinase inhibitor data set recently released by Abbott Laboratories, multitarget activity ridges involving up to 43 different inhibitors and 26 kinase targets were identified. Given the inherently complex architecture of multitarget activity ridges, a new representation format was designed for these ridges based on a scaffold-target matrix. Furthermore, a scoring scheme was developed to identify compounds that were most variably distributed across a multitarget ridge and displayed target differentiation potential. Taken together, our results indicate that multitarget activity ridges represent an attractive data structure for SAR exploration of high-dimensional activity spaces.