Monoacylglycerol lipase (MAGL) terminates the signaling function of the endocannabinoid, 2-arachidonoylglycerol (2-AG). During 2-AG hydrolysis, MAGL liberates arachidonic acid, feeding the principal substrate for the neuroinflammatory prostaglandins. In cancer cells, MAGL redirects lipid stores toward protumorigenic signaling lipids. Thus MAGL inhibitors may have great therapeutic potential. Although potent and increasingly selective MAGL inhibitors have been described, their number is still limited. Here, we have characterized piperazine and piperidine triazole ureas that combine the high potency attributable to the triazole leaving group together with the bulky aromatic benzodioxolyl moiety required for selectivity, culminating in compound JJKK-048 that potently (IC50 < 0.4 nM) inhibited human and rodent MAGL. JJKK-048 displayed low cross-reactivity with other endocannabinoid targets. Activity-based protein profiling of mouse brain and human melanoma cell proteomes suggested high specificity also among the metabolic serine hydrolases.

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A series of commercial phenyl-, heteroaryl-, alkyl-, and alkenylboronic acids were evaluated for their FAAH and MGL inhibitory activities. The compounds were generally selective for FAAH, with IC50 in the nanomolar or low-micromolar range. Eight of these compounds inhibited MGL with IC50 in the micromolar range. The most potent compound, phenylboronic acid with para-nonyl substituent (13), inhibited FAAH and MGL with IC50 of 0.0091 and 7.9 microM, respectively.

Fatty acid amide hydrolase (FAAH) and monoglyceride lipase (MGL) are the main enzymes responsible for the hydrolysis of endogenous cannabinoids N-arachidonoylethanolamide (AEA) and 2-arachidonoylglycerol (2-AG), respectively. Phenyl alkylcarbamates are FAAH inhibitors with anxiolytic and analgesic activities in vivo. Herein we present for the first time the synthesis and biological evaluation of a series of chiral 3-(2-oxazoline)-phenyl N-alkylcarbamates as FAAH inhibitors. Furthermore, the structural background of chirality on the FAAH inhibition is explored by analyzing the protein-ligand interactions. Remarkably, 10-fold difference in potency was observed for (R)-and (S)-derivatives of 3-(5-methyl-4,5-dihydrooxazol-2-yl)phenyl cyclohexylcarbamate (6a vs. 6b). Molecular modelling indicated an important interaction between the oxazoline nitrogen and FAAH active site.

Carbamates are a well-established class of fatty acid amide hydrolase (FAAH) inhibitors. Here we describe the synthesis of meta-substituted phenolic N-alkyl/aryl carbamates and their in vitro FAAH inhibitory activities. The most potent compound, 3-(oxazol-2yl)phenyl cyclohexylcarbamate (2 a), inhibited FAAH with a sub-nanomolar IC(50) value (IC(50)=0.74 nM). Additionally, we developed and validated three-dimensional quantitative structure-activity relationships (QSAR) models of FAAH inhibition combining the newly disclosed carbamates with our previously published inhibitors to give a total set of 99 compounds. Prior to 3D-QSAR modeling, the degree of correlation between FAAH inhibition and in silico reactivity was also established. Both 3D-QSAR methods used, CoMSIA and GRID/GOLPE, produced statistically significant models with coefficient of correlation for external prediction (R(2) (PRED)) values of 0.732 and 0.760, respectively. These models could be of high value in further FAAH inhibitor design.

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The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine Learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide HTVS protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time.In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.