Ligand-Based Combinatorial Design of Selective Purinergic Receptor (A2A) Antagonists Using Self-Organizing Maps

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Abstract
A virtual screening procedure based on a topological pharmacophore similarity metric and self-organizing maps (SOM) was developed and applied to optimizing combinatorial products functioning as P1 purinergic receptor antagonists. The target was the human A2A receptor. A SOM was developed using a set of biologically tested molecules to establish a preliminary structure−activity relationship. A combinatorial library design was performed by projecting virtually assembled new molecules onto the SOM. A small focused library of 17 selected combinatorial products was synthesized and tested. On average, the designed structures yielded a 3-fold smaller binding constant (∼33 vs ∼100 nM) and 3.5-fold higher selectivity (50 vs 14) than the initial library. The most selective compound obtained revealed a 121-fold relative selectivity for A2A with Ki (A2A) = 2.4 nM, and Ki (A1) = 292 nM. This result demonstrates that it was possible to design a small, activity-enriched focused library with an improved property profile using the SOM virtual screening approach. The strategy might be particularly useful in projects in which structure-based design cannot be applied because of a lack of receptor structure information, for example, in the many projects aiming at finding new GPCR modulators.