Molecular scaffolds with high propensity to form multi-target activity cliffs.
In target-dependent activity landscapes of compound series, cliffs are formed by pairs of molecules that are structurally analogous but display significant differences in potency. The detection and analysis of such activity cliffs is a major task in structure-activity relationship analysis and compound optimization. In analogy to activity cliffs, selectivity cliffs can be defined that are formed by structural analogs having significantly different potencies against two targets. The formation of activity cliffs by analogs is generally a consequence of different R-group patterns; e.g., a specific substitution of a given scaffold might increase and another substitution decrease potency. Therefore, activity (or selectivity) cliffs are typically analyzed for a given scaffold representing an analog series, and it has thus far not been explored whether certain scaffolds might display a general tendency to yield compounds forming activity cliffs against different targets. We have exhaustively analyzed scaffolds and associated compound activity data in the ChemblDB and BindingDB databases in order to compare the availability of target-selective scaffolds in these databases and determine whether multi-target activity and multi-target selectivity cliff scaffolds exist. Perhaps unexpectedly, we have identified 143 scaffolds that are represented by multiple compounds and form activity or selectivity cliffs against different targets. These scaffolds have varying chemical complexities and are in part promiscuous binders (i.e., compounds containing these scaffolds bind to distantly related or unrelated targets). However, analogs derived from these scaffolds form steep activity cliffs against different targets. A catalog of scaffolds with high propensity to form activity or selectivity cliffs against multiple targets is provided to help identify potentially promiscuous candidate scaffolds during compound optimization efforts.
Hu
Ye
Y
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universitat, Dahlmannstr 2, D-53113 Bonn, Germany.
Bajorath
Jürgen
J
D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008958:Models, Molecular; D008968:Molecular Conformation; D013329:Structure-Activity Relationship