Computational Analysis of Multi-target Structure-Activity Relationships to Derive Preference Orders for Chemical Modifications toward Target Selectivity

For series of compounds with activity against multiple targets, the resulting multi‐target structure–activity relationships (mtSARs) are usually difficult to analyze. However, rationalizing mtSARs is of great importance for the development of compounds that are selective for one target over closely related ones. Herein we present a methodological framework for the study of mtSARs and identification of substitution sites in analogue series that are selectivity determinants. Active analogues are subjected to uniform R‐group decomposition, compared on the basis of pharmacophore feature edit distances, and organized in previously reported tree‐like structures that we adapted for mtSAR analysis. These data structures represent a substitution site hierarchy, capture potency variations, and reflect patterns of SAR discontinuity. Generating this data structure for multiple targets makes it possible to determine preference orders for chemical modifications to improve target selectivity. Accordingly, high emphasis is put on the derivation of simple rules to design substitutions that are likely to yield target‐selective compounds. Furthermore, the analysis is applicable to identify both additive and non‐additive effects on compound activity and selectivity as a consequence of multi‐site substitutions. Selectivity: A computational methodology is presented for the analysis of multi‐target structure–activity relationships of compound series that ultimately aims at the identification of selectivity determinants. The approach provides a basis for the formulation of intuitive rules for the design of target‐selective compounds. Shown is the derivation of preference orders of pharmacophore features at a defined substitution site in an analogue series.