Computational method for the identification of third generation activity cliffs.

In medicinal chemistry and chemoinformatics, activity cliffs (ACs) are defined as pairs of structurally similar compounds that are active against the same target but have a large difference in potency. Accordingly, ACs are rich in structure-activity relationship (SAR) information, which rationalizes their relevance for medicinal chemistry. For identifying ACs, a compound similarity criterion and a potency difference criterion must be specified. So far a constant potency difference between AC partner compounds has mostly been set, e.g. 100-fold, irrespective of the specific activity (targets) of cliff-forming compounds. Herein, we introduce a computational methodology for AC identification and analysis that includes three novel components: •ACs are identified on the basis of variable target set-dependent potency difference criteria (a 'target set' represents a collection of compounds that are active against a given target protein).•ACs are extracted from computationally determined analog series (ASs) and consist of pairs of analogs with single or multiple substitution sites.•For multi-site ACs, a search for analogs with individual substitutions is performed to analyze their contributions to AC formation and determine if multi-site ACs can be represented by single-site ACs.