The family of adenosine receptors (ARs) is focus of several medicinal
chemistry programs aimed to find new potent and selective drugs. Each receptor
subtype has been proposed as a relevant drug target in the treatment of, e.g., cardiovascular
or inflammatory diseases, asthma or Parkinson’s disease. Until recently,
most of these efforts have been dominated by ligand-based or empirical approaches.
However, the latest advances in G protein-coupled receptor (GPCR)
crystallography allowed for a thorough structural characterization of the A2AAR
subtype, which has been crystalized with a number of agonists and antagonists.
Consequently, the ligand discovery of AR ligands has been enriched with a number
of structure-based approaches. These include the generation of higher-confident
homology models for the remaining AR subtypes, virtual screening identification of novel chemotypes,
structure-based lead-optimization programs, rationalization of selectivity profiles, or the structural
characterization of novel binding sites that enable the design of novel allosteric modulators.
Computational methodologies have importantly contributed to the success of these structure-based
approaches, and the recent advances in the field are also analyzed in this review. We conclude that
the design of adenosine receptor ligands has improved dramatically with the consideration of structure-
based approaches, which is paving the way to a better understanding of the biology and pharmacological
modulation of this relevant family of receptors.