Quantitative Structure-selectivity Relationship Research Articles

Computational workflow for steric assessment using the electric field-derived size

Molecular structure plays an important role in the selectivity and performance of catalysts. Understanding the impact of structural differences on catalyst performance via quantitative structure-selectivity relationships is key to developing high-performing catalytic systems. There are several methods that have been introduced to quantify steric contributions, including Tolman cone angles, Charton parameters, and A-values. While these have shown promise in predicting selectivity, they access similar, general steric contributions and are largely empirically derived. Alternatively, Sterimol parameters offer a specific multi-directional measure of steric bulk in the form of three vectors in units of distance. Recently, these parameters revealed strong correlations between structure and selectivity in asymmetric catalysis. Yet, despite their demonstrated performance, Sterimol parameters are commonly derived using van der Waals radii, which approximate molecular size using hard-spheres. This method may not accurately describe highly polarized systems. Recently, a new chemical system size metric based on the electric-field of a molecule was developed, which accesses the occupied space of a molecule. Here, we demonstrate that the electric field-derived Sterimol parameters reveal similar structure-selectivity relationships in asymmetric catalysis as conventional Sterimol parameters. Specifically, we present a computational workflow for calculating Sterimol parameters based on the size of a molecule’s electric field, and validate our method using several asymmetric catalysis reactions.

Catalytic asymmetric indolization by a desymmetrizing [3 + 2] annulation strategy.

A new catalytic asymmetric indolization reaction by a desymmetrizing [3 + 2] annulation strategy is developed. The reaction proceeds via a rhodium-catalyzed enantioposition-selective addition/5-exo-trig cyclization/dehydration cascade between ortho-amino arylboronic acids and 2,2-disubstituted cyclopentene-1,3-diones to produce N-unprotected cyclopenta[b]indoles bearing an all-carbon quaternary stereocenter in high yields with good enantioselectivities. A quantitative structure-selectivity relationship (QSSR) model was established to identify the optimal chiral ligand, which effectively controlled the formation of the stereocenter away from the reaction site. Density functional theory (DFT) calculations, non-covalent interaction analysis, and Eyring analysis were performed to understand the key reaction step and the function of the ligand.

Molecular Descriptors and QSSR Models in Asymmetric Catalysis

Abstract: The 2021 Nobel Prize in Chemistry was awarded to small molecule asymmetric organocatalysis. Quantitative structure−selectivity relationships (QSSR) based on linear free energy relationships are crucial to predicting enantioselectivity in asymmetric catalytic reactions and to gaining insight into the catalytic mechanism. Molecular descriptors describing steric effects and/or electronic effects are described in this review. Subsequently, QSSR models in enantioselective catalysis are analyzed and prospected.

Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids

AbstractPredicting enantioselectivities in asymmetric catalytic reactions through chemometric approach is a challenging area. In this paper, quantitative structure−selectivity relationship (QSSR) models were successfully developed for thiol addition to N‐acylimines catalyzed by chiral phosphoric acids. Ten Dragon molecular descriptors calculated from thiols and phosphoric acid catalysts were used to correlate with 1075 enantioselectivities ΔΔG. Machine learning algorithms, support vector machine (SVM) and random forest (RF), were adopted to build QSSRs after descriptor subset selection based on the multiple linear regression technique. The SVM and RF models, respectively, have coefficient of determination R2 of 0.925 and 0.944 for the training set, and of 0.837 and 0.851 for the test set. Both SVM and RF models were subjected to internal and external validation and estimation in prediction ability and systematic error.

Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis

Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. Although machine-learning-based optimization of molecular catalysis is a rapidly growing research field, the use of such approaches for catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α- C -allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular-field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This chemoselective and stereodivergent, asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives. Practical regression-based, data-driven, chiral catalyst design and optimization method 3-Dimensional, quantitative structure-selectivity relationships and molecular field analysis Control/improvement of four reaction outcomes with regression analysis Stereodivergent asymmetric synthesis of α-allyl carboxylic acids Chen et al. report that data-driven catalyst design facilitates stereodivergent asymmetric synthesis, which remains challenging in organic synthesis. This research indicates that digital transformations (DXs) of organic synthesis (e.g., the use of machine-leaning and materials-informatics techniques) enable analysis and control of complicated reactions, opening new avenues in molecular catalysis.

Mechanistic study of enantioselective Pd-catalyzed C(sp3)-H activation of thioethers involving two distinct stereomodels.

Enantioselective C(sp3)-H activation has gained considerable attention from the synthetic chemistry community. Despite the intense interest in these reactions, the mechanisms responsible for enantioselection are still vague. In the course of the development of aryl thioether-directed C(sp3)-H arylation, we noticed extreme variation in sensitivity of two substrate classes to substituent effects of ligands and directing groups: whereas 3-pentyl sulfides (prochiral α-center) responded positively to substitution on ligands and directing groups, isobutyl sulfides (prochiral β-center) were entirely insensitive. Quantitative structure selectivity relationship (QSSR) analyses of directing group and ligand substitution and the development of a new class of mono-N-acetyl protected amino anilamide (MPAAn) ligands led to high enantiomeric ratios (up to 99:1) for thioether-directed C(sp3)-H arylation. Key to the realization of this method was the exploitation of transient chirality at sulfur, which relays stereochemical information from the ligand backbone to enantiotopic carbons of the substrate in a rate- and enantio-determining cyclometallation deprotonation. The absolute stereochemistry of the products for these two substrates were revealed to be opposite. DFT evaluation of all possible diastereomeric transition states confirmed initial premises that guided rational ligand and directing group design. The implications of this study will assist in the further development of enantioselective C(sp3)-H activation, namely by highlighting the non-innocence of directing groups, distal steric influences, and the delicate interplay between steric Pauli repulsion and London dispersion in enantioinduction.

A Conformer‐Dependent, Quantitative Quadrant Model

AbstractThis study reports the development and application of a conformer‐dependent quantitative quadrant descriptor for generating models that relate catalyst performance to catalyst structure in enantioselective transformations. The generality of these descriptors is demonstrated by using them for three different reactions: (1) copper‐catalyzed, enantioselective cyclopropanation of alkenes, (2) rhodium‐catalyzed enantioselective hydrogenation of α‐substituted N‐acyl‐enamides, and (3) enantioselective addition of thiols to N‐acyl imines. This work will provide researchers an interpretable steric descriptor that merges the heuristic value of quadrant models with a quantitative tool that can be used to create statistically meaningful correlations between the steric occupancy of catalyst quadrants and stereoselectivity. The low dimensionality of this descriptor, its ability to capture conformational effects and stereostructure, and its direct relationship to intuitive structural properties make it particularly well‐suited for creating Quantitative Structure‐Selectivity Relationships (QSSR) with smaller datasets of asymmetric reactions.

Systematic In Silico Design, Synthesis, and Biological Studies of Some Novel 1,4-Benzoquinone Derivatives for the Prospective Management of Cognitive Decline.

Cholinesterases are significant biological targets for the regulation of cholinergic neurotransmission, and their inhibitors are being exploited for the management of cognitive decline in various neurological conditions. The 1,4-benzoquinone scaffold possesses antioxidant potential along with AChE inhibition activity in various neurological disorders. To design novel and potent selective 1,4-benzoquinone analogues as cholinesterase inhibitors, a ligand-based drug design strategy was followed to develop a 3D quantitative structure-selectivity relationship (QSSR) model. On the basis of the best fit model, eight novel 1,4-benzoquinone derivatives were designed and synthesized implementing appropriate synthetic procedures and were characterized by various spectral and elemental techniques. All the synthesized compounds were evaluated for their selective in vitro acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory potential at different concentrations using mice brain homogenate as the source of the enzyme. Out of these compounds, the three most selective compounds were further evaluated for behavioral variations using step down passive avoidance and escape learning procedure at a dose of 0.5 mg/kg taking donepezil as the reference drug. Biochemical estimation of the markers of oxidative stress (lipid peroxidation, superoxide dismutase, glutathione, and catalase) has also been carried out to determine the role of the synthesized molecules on the scopolamine induced oxidative damage. Compound 2a displayed appreciable selectivity index values as predicted through the 3D-QSSR model. Further, docked complexes of compound 2a with AChE and BChE were subjected to molecular dynamic simulations for a period of 30 ns to study the orientations and stable conformations of the most active molecules in the catalytic domain of these enzymes. The results obtained from the 3D-QSSR analysis, docking, and molecular dynamic studies were found to be appreciable and provided a deep insight into the structural features required for the selectivity of AChE inhibitors over BChE. The outcome of this study may be used as a novel tool to design new highly selective and more potent molecules.

Asymmetric Hydrogenation of Cationic Intermediates for the Synthesis of Chiral N,O-Acetals.

For over half a century, transition-metal-catalyzed homogeneous hydrogenation has been mainly focused on neutral and readily prepared unsaturated substrates. Although the addition of molecular hydrogen to C=C, C=N, and C=O bonds represents a well-studied paradigm, the asymmetric hydrogenation of cationic species remains an underdeveloped area. In this study, we were seeking a breakthrough in asymmetric hydrogenation, with cationic intermediates as targets, and thereby anticipating applying this powerful tool to the construction of challenging chiral molecules. Under acidic conditions, both N- or O-acetylsalicylamides underwent cyclization to generate cationic intermediates, which were subsequently reduced by an iridium or rhodium hydride complex. The resulting N,O-acetals were synthesized with remarkably high enantioselectivity. This catalytic strategy exhibited high efficiency (turnover number of up to 4400) and high chemoselectivity. Mechanistic studies supported the hypothesis that a cationic intermediate was formed in situ and hydrogenated afterwards. A catalytic cycle has been proposed with hydride transfer from the iridium complex to the cationic sp2 carbon atom being the rate-determining step. A steric map of the catalyst has been created to illustrate the chiral environment, and a quantitative structure-selectivity relationship analysis showed how enantiomeric induction was achieved in this chemical transformation.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

Retooling Asymmetric Conjugate Additions for Sterically Demanding Substrates with an Iterative Data-Driven Approach.

The development of catalytic enantioselective methods is routinely carried out using easily accessible and prototypical substrates. This approach to reaction development often yields asymmetric methods that perform poorly using substrates that are sterically or electronically dissimilar to those used during the reaction optimization campaign. Consequently, expanding the scope of previously optimized catalytic asymmetric reactions to include more challenging substrates is decidedly nontrivial. Here, we address this challenge through the development of a systematic workflow to broaden the applicability and reliability of asymmetric conjugate additions to substrates conventionally regarded as sterically and electronically demanding. The copper-catalyzed asymmetric conjugate addition of alkylzirconium nucleophiles to form tertiary centers, although successful for linear alkyl chains, fails for more sterically demanding linear α,β-unsaturated ketones. Key to adapting this method to obtain high enantioselectivity was the synthesis of modified phosphoramidite ligands, designed using quantitative structure–selectivity relationships (QSSRs). Iterative rounds of model construction and ligand synthesis were executed in parallel to evaluate the performance of 20 chiral ligands. The copper-catalyzed asymmetric addition is now more broadly applicable, even tolerating linear enones bearing tert-butyl β-substituents. The presence of common functional groups is tolerated in both nucleophiles and electrophiles, giving up to 99% yield and 95% ee across 20 examples.

Formation of quaternary centres by copper catalysed asymmetric conjugate addition to β-substituted cyclopentenones with the aid of a quantitative structure-selectivity relationship.

A new asymmetric conjugate addition method was developed for β-substituted cyclopentenones to form quaternary centres using alkylzirconocene nucleophiles giving up to 97% yield and 92% ee. Key to the reaction's success was the design of suitable phosphoramidite ligands which was aided by a linear quantitative structure-selectivity relationship (QSSR). QSSR models were created from the ligand screening data (a total of 36 ligands) which revealed important electronic and steric requirements and led to the synthesis of more enantioselective ligands. DFT calculations of competing transition structures enable the interpretation of the electronic and steric terms present in the QSSR models.

Quantitative structure‐selectivity relationship (QSSR)‐based molecular insight into the cross‐reactivity and specificity of chemotherapeutic inhibitors between PI3Kα and PI3Kβ

Selective inhibition of phosphoinositide 3‐kinase (PI3K) isoforms α and β with small‐molecule inhibitors can result in distinct biological effects on anticancer chemotherapy. However, many existing PI3K inhibitors have moderate or high promiscuity and cross‐reactivity between the 2 kinase isoforms. Here, a quantitative structure‐selectivity relationship–based statistical modeling scheme was used to characterize the relative contribution of independent kinase residues to inhibitor selectivity and to predict the selectivity and specificity for existing PI3K inhibitors. It is found that the residue type and distribution of kinase's active site play an important role in inhibitor selectivity, while rest of the kinase may contribute to the selectivity through long‐range chemical interactions and indirect allosteric effect. The selectivity is also determined by the configuration difference between PI3Kα and PI3Kβ kinase domains. Larger inhibitor compounds have lower binding potency to PI3Kβ than PI3Kα and thus possess higher selectivity for PI3Kα over PI3Kβ.

Interpretable linear and nonlinear quantitative structure-selectivity relationship (QSSR) modeling of a biomimetic catalytic system by particle swarm optimization based sparse regression

A particle swarm optimization (PSO) based sparse regression (PSO-SR) strategy was proposed to study the quantitative structure-selectivity relationship (QSSR) of a biomimetic catalytic system, where the selectivity in the mild oxidation of o-nitrotoluene to o-nitrobenzaldehyde was related to the molecular descriptors of 48 metalloporphyrin catalysts. PSO was used to obtain an optimal variable combination for linear or nonlinear models. For nonlinear modeling, a set of 44 nonlinear transforms were developed for each single descriptor. To enable model interpretability and reduce the risk of overfitting, the total descriptors were divided into subclasses and the selected variables were forced to be sparsely distributed in each subclass. Model complexity was controlled by adjusting the maximum total number of variables included. Accurate linear and nonlinear PSO-SR models were developed using multiple linear regression (MLR) and partial least squares (PLS) and validated by randomly and repeatedly splitting the data into training and test objects for 500 times. The best predictions were obtained with 10 variables with linear (Q2=0.9460) and nonlinear (Q2=0.9505) models. The results indicate PSO-SR could provide an effective and useful strategy for modeling and interpreting complex QSSR problems. The proposed nonlinear modeling method could provide more information for model interpretation by probing and catching the unknown nonlinear relationship between a descriptor and the observed selectivity.

Quantitative Two-Dimensional (2D) Morphology–Selectivity Relationship of CoMoS Nanolayers: A Combined High-Resolution High-Angle Annular Dark Field Scanning Transmission Electron Microscopy (HR HAADF-STEM) and Density Functional Theory (DFT) Study

Cobalt-promoted and nonpromoted MoS2 nanolayers supported on alumina are prepared and activated under various sulfidation (temperature/pressure (T, P)) conditions which induce the formation of nanolayers with two-dimensional (2D) morphology of MoS2 tuned by the presence of the promoter and by the sulfidation conditions. An unprecedented high selectivity is found for the CoMoS nanolayers. The origin of this selectivity is explained by 2D morphology effects quantified by high-resolution scanning transmission electron microscopy in high-angle annular dark field mode (HR HAADF-STEM) and density functional theory (DFT) calculations. A quantitative structure–selectivity relationship is identified between the 2D shape index of CoMoS nanolayers and their selectivity performances. This 2D shape index is determined by statistical analysis of the CoMoS nanolayers identified after principal component analysis processing of HR HAADF-STEM images. It is shown that this shape index, reflecting the isotropic/anisotropic d...

The selectivity and promiscuity of brain-neuroregenerative inhibitors between ROCK1 and ROCK2 isoforms: An integration of SB-QSSR modelling, QM/MM analysis and in vitro kinase assay

The Rho-associated kinases (ROCKs) have long been recognized as an attractive therapeutic target for various neurological diseases; selective inhibition of ROCK1 and ROCK2 isoforms would result in distinct biological effects on neurogenesis, neuroplasticity and neuroregeneration after brain surgery and traumatic brain injury. However, the discovery and design of isoform-selective inhibitors remain a great challenge due to the high conservation and similarity between the kinase domains of ROCK1 and ROCK2. Here, a structure-based quantitative structure–selectivity relationship (SB-QSSR) approach was used to correlate experimentally measured selectivity with the difference in inhibitor binding to the two kinase isoforms. The resulting regression models were examined rigorously through both internal cross-validation and external blind validation; a nonlinear predictor was found to have high fitting stability and strong generalization ability, which was then employed to perform virtual screening against a structurally diverse, drug-like compound library. Consequently, five and seven hits were identified as promising candidates of 1-o-2 and 2-o-1 selective inhibitors, respectively, from which seven purchasable compounds were tested in vitro using a standard kinase assay protocol to determine their inhibitory activity against and selectivity between ROCK1 and ROCK2. The structural basis, energetic property and biological implication underlying inhibitor selectivity and promiscuity were also investigated systematically using a hybrid quantum mechanics/molecular mechanics (QM/MM) scheme.

Selective inhibition of ROCK kinase isoforms to promote neuroregeneration after brain surgery

The Rho-associated protein kinase (ROCK) is an essential regulator of Rho signaling pathways in a variety of cellular processes, which has been established as a potential therapeutic target to promote neuroregeneration after brain surgery. The kinase family contains two highly homologous, tissue-specific isoforms ROCK1 and ROCK2, and selective inhibition of them would result in distinct biological effects in neurotherapy. The two kinase isoforms have only a slight difference across their active sites, but structural components out of the sites seem to confer exquisite specificity for selective binding of inhibitor ligands to ROCK1 and ROCK2. Here, a quantitative structure–selectivity relationship model is developed to characterize the relative contribution of each kinase residue to inhibitor selectivity. The model was then used to screen against a panel of known kinase inhibitors, from which five compounds with high theoretical selectivity are tested in vitro using a kinase assay protocol to determine their biological activity and selectivity. Consequently, the compound CAY10576 is found to have moderate and high inhibitory potencies against ROCK1 and ROCK2 (IC50 = 1.8 and 0.04 μM, respectively) as well as strong selectivity between the two isoforms (S = −1.65), which could be considered as a good lead entity to develop potent, selective ROCK inhibitors to promote neurogenesis and neuroregeneration.

Design and prediction of new anticoagulants as a selective Factor IXa inhibitor via three-dimensional quantitative structure-property relationships of amidinobenzothiophene derivatives.

Factor IXa (FIXa), a blood coagulation factor, is specifically inhibited at the initiation stage of the coagulation cascade, promising an excellent approach for developing selective and safe anticoagulants. Eighty-four amidinobenzothiophene antithrombotic derivatives targeting FIXa were selected to establish three-dimensional quantitative structure–activity relationship (3D-QSAR) and three-dimensional quantitative structure–selectivity relationship (3D-QSSR) models using comparative molecular field analysis and comparative similarity indices analysis methods. Internal and external cross-validation techniques were investigated as well as region focusing and bootstrapping. The satisfactory q2 values of 0.753 and 0.770, and r2 values of 0.940 and 0.965 for 3D-QSAR and 3D-QSSR, respectively, indicated that the models are available to predict both the inhibitory activity and selectivity on FIXa against Factor Xa, the activated status of Factor X. This work revealed that the steric, hydrophobic, and H-bond factors should appropriately be taken into account in future rational design, especially the modifications at the 2′-position of the benzene and the 6-position of the benzothiophene in the R group, providing helpful clues to design more active and selective FIXa inhibitors for the treatment of thrombosis. On the basis of the three-dimensional quantitative structure–property relationships, 16 new potent molecules have been designed and are predicted to be more active and selective than Compound 33, which has the best activity as reported in the literature.

The small-molecule inhibitor selectivity between IKKα and IKKβ kinases in NF-κB signaling pathway

The enzyme complex IκB kinase (IKK) is an essential activator of NF-κB signaling pathway involved in propagating the cellular response to inflammation. The complex contains two functional subunits IKKα and IKKβ, which are structurally conserved kinases and selective inhibition of them would result in distinct biological effects. However, most existing IKK inhibitors show moderate or high promiscuity for the two homologous kinases. Understanding of the molecular mechanism and biological implication underlying the specific interactions in IKK–ligand recognition is thus fundamentally important for the rational design of selective IKK inhibitors. In the current work, we integrated molecular docking, quantum mechanics/molecular mechanics calculation and Poisson–Boltzmann/surface area analysis to investigate the structural basis and energetic property of the selective binding of small-molecule ligands to IKKα and IKKβ. It was found that the selectivity is primarily determined by the size and topology difference in ATP-binding pocket of IKKα and IKKβ kinase domains; bulky inhibitor molecules commonly have, respectively, low and appropriate affinities towards IKKα and IKKβ, and thus exhibit relatively high selectivity for IKKβ over IKKα, whereas small ligands can only bind weakly to both the two kinases with low selectivity. In addition, the conformation, arrangement and distribution of residues in IKK pockets are also responsible for constituting the exquisite specificity of ligand binding to KKα and IKKβ. Next, a novel quantitative structure–selectivity relationship model was developed to characterize the relative contribution of each kinase residue to inhibitor selectivity and to predict the selectivity and specificity for a number of known IKK inhibitors. Results showed that the active-site residues contribute significantly to the selectivity by directly interacting with inhibitor ligands, while those protein portions far away from the kinase active sites may also play an important role in determining the selectivity through long-range non-bonded forces and indirect allosteric effect.

Structural and energetic insights into the selective interactions of monoacylglycerol lipase with its natural substrate and small-molecule inhibitors

The monoacylglycerol lipase (MAGL) regulates 2-arachidonoyl glycerol (2-AG) level in the endocannabinoid system (ECS), which is implicated in a number of severe diseases such as cancer and Alzheimer’s disease. However, most existing MAGL inhibitors also show additional inhibitory effects on fatty acid amide hydrolase (FAAH), another member of the ECS that degrades the 2-AG analog N-arachidonoyl ethanolamine. Understanding of molecular mechanism and biological implication underlying the specific interactions in MAGL–ligand recognition is thus fundamentally important for the rational design of selective MAGL inhibitors. In the current study, the structural basis and energetic property regarding the binding of several MAGL inhibitors as well as its substrate 2-AG to both the MAGL and FAAH are investigated systematically by integrating molecular docking, quantum mechanics/molecular mechanics analysis, and Poisson–Boltzmann/surface area solvent model. In addition, a novel quantitative structure–selectivity relationship method is proposed to help in the explanation and prediction of inhibitor selectivity between MAGL and FAAH. It is suggested that the selectivity is primarily determined by the size, topology, and property of the rear moiety of inhibitor compounds; a bulky, bifurcated rear is the prerequisite for a inhibitor to have high selectivity for MAGL over FAAH, whereas those dual-type MAGL–FAAH inhibitors should possess a small, rear moiety—the ideal choice is a single aromatic branch occupying this position.