Using Novel Descriptor Accounting for Ligand–Receptor Interactions To Define and Visually Explore Biologically Relevant Chemical Space

The definition and pragmatic implementation of biologically
relevant
chemical space is critical in addressing navigation strategies in
the overlapping regions where chemistry and therapeutically relevant
targets reside and, therefore, also key to performing an efficient
drug discovery project. Here, we describe the development and implementation
of a simple and robust method for representing biologically relevant
chemical space as a general reference according to current knowledge,
independently of any reference space, and analyzing chemical structures
accordingly. Underlying our method is the generation of a novel descriptor
(LiRIf) that converts structural information into a one-dimensional
string accounting for the plausible ligand–receptor interactions
as well as for topological information. Capitalizing on ligand–receptor
interactions as a descriptor enables the clustering, profiling, and
comparison of libraries of compounds from a chemical biology and medicinal
chemistry perspective. In addition, as a case study, R-groups analysis
is performed to identify the most populated ligand–receptor
interactions according to different target families (GPCR, kinases,
etc.), as well as to evaluate the coverage of biologically relevant
chemical space by structures annotated in different databases (ChEMBL,
Glida, etc.).