Representation of multi-target activity landscapes through target pair-based compound encoding in self-organizing maps.

Activity landscape representations provide access to structure-activity relationships information in compound data sets. In general, activity landscape models integrate molecular similarity relationships with biological activity data. Typically, activity against a single target is monitored. However, for steadily increasing numbers of compounds, activity against multiple targets is reported, resulting in an opportunity, and often a need, to explore multi-target structure-activity relationships. It would be attractive to utilize activity landscape representations to aid in this process, but the design of activity landscapes for multiple targets is a complicated task. Only recently has a first multi-target landscape model been introduced, consisting of an annotated compound network focused on the systematic detection of activity cliffs. Herein, we report a conceptually different multi-target activity landscape design that is based on a 2D projection of chemical reference space using self-organizing maps and encodes compounds as arrays of pair-wise target activity relationships. In this context, we introduce the concept of discontinuity in multi-target activity space. The well-ordered activity landscape model highlights centers of discontinuity in activity space and is straightforward to interpret. It has been applied to analyze compound data sets with three, four, and five target annotations and identify multi-target structure-activity relationships determinants in analog series.