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.
Wassermann
Anne Mai
AM
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr 2, 53113 Bonn, Germany.
Peltason
Lisa
L
Bajorath
Jürgen
J
D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D015195:Drug Design; D007700:Kinetics; D047428:Protein Kinase Inhibitors / Q000737:chemistry; D015842:Serine Proteinase Inhibitors / Q000737:chemistry; D013329:Structure-Activity Relationship
D047428:Protein Kinase Inhibitors; D015842:Serine Proteinase Inhibitors