SUMMARY: Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (K _i = 0.7 µM) led to ‘4042 , a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of ‘4042 ( ‘1350 ) in complex with CB1R-G _i1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.

A new approach to synthesize cannabilactones using Suzuki cross-coupling reaction followed by one-step demethylation-cyclization is presented. The two key cannabilactone prototypes AM1710 and AM1714 were obtained selectively in high overall yields and in a lesser number of synthetic steps when compared to our earlier synthesis. The new approach expedited the synthesis of cannabilactone analogs with structural modifications at the four potential pharmacophoric regions.

Cannabinoid receptors are both therapeutically attractive and are interesting model systems for structure-based methods. Here we investigated topical questions in library docking using the CB2 receptor. While a CB1R docking campaign found potent but nonselective ligands, here subtype selective ligands were found by targeting polar residues. Hit rates and hit affinities improved with library size, but docking against active and inactive receptor states did not reliably bias toward agonists or antagonists. Cryo-EM structures of two of the new agonists superposed well on the docking predictions. Structure-based optimization led to 10- to 140-fold improvements within three series, consistent with well-behaved ligands. Hit rates with an explicit 2.6 billion molecule library resembled those of an implied 11 billion molecule library from a building-block method, supporting the latter's ability to explore this space, though higher affinities were discovered from the explicit set. Implications for future studies are considered.