Bioactivity landscape modeling: chemoinformatic characterization of structure-activity relationships of compounds tested across multiple targets.

Characterizing structure-activity relationships (SAR) of sets of compounds screened across different targets is crucial in several drug discovery endeavors. To this end, chemoinformatic approaches are emerging to characterize SARs using the concept of multi-target activity landscapes. Herein, we present the Structure multiple Activity Similarity (SmAS) maps and the Structure multiple Activity Landscape Index (SmALI) as general approaches to navigate through and quantify the most informative regions of multi-target activity landscapes. These methods are extensions of SAS maps and SALI metric used for single targets. To illustrate the use of these methods, SmAS maps and SmALI values were employed for characterizing the SAR of three benchmark sets of compounds screened with different target families. As a follow up of our work, we employed four 2D and 3D structure representations to obtain consensus models for each data set. For the three data sets, we identified pairs of compounds with high structure similarity but very different bioactivity profile across the corresponding targets of each family that is, multi-target activity cliffs. Also, we identified pairs of compounds with low structure similarity but similar bioactivity profile across the different targets that is, multi-target scaffold hops. The consensus SmAS maps and mean SmALI metric are complementary chemoinformatic tools to systematically describe multi-target activity landscapes.