Computational Studies toward the Identification of CB2R-M1R Dual Modulators
Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, 108609Jordan University of Science and Technology, Irbid 22110, Jordan
Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman 11733, Jordan
Department of Computer Science, Faculty of Computer and Information Technology, Jordan University of Science and Technology, Irbid 22110, Jordan
Instituto de Química Médica, Consejo Superior de Investigaciones Científicas (IQM-CSIC), Madrid 28006, Spain
*Email: ihisawi@just.edu.jo. *Email: paula.morales@iqm.csic.es.Abstract
The complex and multifactorial nature of different neurodegenerative disorders hampers the capacity to identify effective treatments. Therefore, instead of relying solely on monotherapies or combination therapies, which typically come with dosing complications and limited synergy, multitarget-directed ligand strategies have emerged as one of the most dynamic and promising approaches to improve outcomes for such diseases. This study sought to identify dual modulators that specifically target cannabinoid receptor type 2 (CB2R) and muscarinic acetylcholine receptor subtype 1 (M1R), two receptors involved in various physiological and neurological processes and frequently implicated in disorders like Alzheimer’s, Parkinson’s, and chronic pain. Herein, we utilized a comprehensive computational pipeline starting with a network pharmacology analysis to map the pharmacological landscape of the dual-targeted ligands. Thereafter, molecular descriptors were employed to uncover structural similarities between CB2R agonists and M1R-positive allosteric modulators. Promising candidates were further evaluated for their binding affinities to the corresponding receptors by molecular docking studies. Collectively, these integrated computational approaches yielded a shortlist of chemotypes with the potential for dual regulation of CB2R and M1R. These findings provide a computational foundation and potential chemical starting points for future experimental studies aimed at exploring CB2R–M1R dual modulation in intricate neurodegenerative disorders and related conditions.
Introduction
The intricate and multifactorial origins of various neurodegenerative disorders diminish the likelihood of identifying suitable treatments.ref1 These disorders often involve a convergence of complex molecular pathways, cellular dysfunctions, and widespread neuroinflammation, which collectively diminish the efficacy of conventional treatment strategies. ref1,ref2 Consequently, reliance solely on traditional therapeutic approaches, such as monotherapies or combination therapies, often proves inadequate due to suboptimal dosing regimens, adverse drug interactions, and insufficient synergy.ref3 In light of these limitations, multitarget directed ligands (MTDLs) have emerged as a promising therapeutic strategy. MTDLs can offer synergistic effects, reduce side effects, and improve pharmacokinetic profiles by simultaneously addressing numerous critical pathways related to the same disease, in contrast to single-target approaches. ref4−ref5 ref6 This strategy could tackle the multifaceted nature of neurodegenerative diseases more holistically and effectively. ref2,ref7,ref8
This study focuses on the computational identification and rational design of dual modulators targeting two prominent G-protein-coupled receptors (GPCRs): the cannabinoid receptor type 2 (CB2R) and the muscarinic acetylcholine receptor subtype 1 (M1R). Both receptors are critically involved in various physiological and neurological processes and frequently implicated in disorders like Alzheimer’s, Parkinson’s, and chronic pain. ref9−ref10 ref11 ref12 ref13 ref14
Alzheimer’s Disease (AD) is the leading cause of dementia among older patients, affecting more than 35 million people worldwide.ref15 The disease is marked by a highly multifactorial and complex pathophysiology involving numerous etiopathogenic mechanisms, such as the abnormal buildup of β-amyloid plaques and neurofibrillary tangles.ref15 In addition to disruptions across multiple neurotransmitter systems, in particular the endocannabinoid and cholinergic systems are notably impaired during AD progression. ref9,ref11,ref16
The endocannabinoid system, in particular CB2R, has been closely linked to AD. CB2R, which is predominantly expressed in the central nervous system, is significantly upregulated in AD patients and shows a positive correlation with β-amyloid plaque accumulation. ref9−ref10 ref11 Activation of CB2R has demonstrated a reduction in AD pathogenesis by mitigating neuroinflammation and neurodegenerationref17 (Figure fig1 A). For instance, the CB2R agonist JWH-133 proved its capability to improve memory and augment the brain’s intrinsic repair mechanisms in AD rat models (Figure fig1 B).ref18 AM1241 nanoparticles, another CB2R agonist, efficiently mitigated neurodegenerative pathology and promoted forebrain neurogenesis in AD mouse models, collectively restoring learning and memory capabilities.ref19
Similarly, the cholinergic system plays a vital role in AD. According to the cholinergic hypothesis, the imbalance of acetylcholine content contributes to the cognitive decline in AD patients. ref20−ref21 ref22 Among the muscarinic receptor subtypes, M1R has emerged as a particularly promising route due to its ability to modulate cholinergic neurotransmission. ref16,ref23,ref24 However, the high structural similarity across all five muscarinic receptor subtypes at the orthosteric binding site makes the identification of selective M1R agonists challenging, and the lack of selectivity would be paid off as adverse effects. ref16,ref25 As a result, targeting allosteric sites has become an innovative approach to attain receptor subtype selectivity. ref16,ref26,ref27 In AD animal models, selective M1R-positive allosteric modulators (PAMs) have been shown not only to alleviate cognitive dysfunction but also to attenuate AD pathogenesis through the modulation of neuroinflammation and neurodegeneration (Figure fig1 A). ref28,ref29 Notably, pure M1R PAMs, such as VU6007496, demonstrated superior functional engagement compared to M1R Ago-PAMs like MK-7622 and PF-06827443, resulting in cognitive enhancement without significant adverse events (Figure fig1 C). ref16,ref30
In addition to each receptor’s role in AD, a growing body of evidence indicates that crosstalk occurs between the endocannabinoid and cholinergic systems in AD. ref31−ref32 ref33 Cannabinoid receptors have been demonstrated to control cholinergic dysregulation indirectly, resulting in enhanced cognitive function in AD models. Moreover, there is evidence that anandamide, an endocannabinoid fatty acid neurotransmitter, can act on both targets, functioning as a CB2R agonist and M1R PAM. ref34−ref35 ref36 ref37 A separate investigation indicated that the dual role of anandamide may underlie the noted improvements in cognitive performance and the attenuation in the onset of AD markers in experimental models.ref38
In light of the growing interest in MTDLs as next-generation therapies,ref7 dual ligands that can concurrently modulate CB2R and M1R represent a novel and compelling approach. Such modulators could harness synergistic effects across two dysregulated neurotransmitter systems, potentially offering novel therapeutic outcomes desirable for treating specific neurodegenerative disorders, including AD, Parkinson’s disease, and pain-related disorders. ref12−ref13 ref14 However, given that no small molecule has yet been experimentally confirmed to act as a dual CB2R agonist and M1R PAM, a computational exploration of their shared chemical space can provide a valuable preliminary framework for future discovery. While numerous CB2R agonist chemotypes have been recorded, ref39,ref40 selective M1R PAMs remain comparatively scarce. ref13,ref16,ref26
Accordingly, this work presents an integrative computational pipeline that combines multiple complementary strategies to identify and prioritize potential dual CB2R–M1R modulators (Figure fig2 ). The framework is designed as a methodological and data resource to support future experimental and computational investigations of CB2R–M1R multitarget modulation. The workflow began with a network pharmacology analysis to map the molecular interactions of dual-target ligands with relative proteins and genes, thereby supporting the therapeutic rationale for the concurrent modulation of both receptors. Principal component analysis (PCA) was then used to identify the overlapping chemical space between CB2R agonists and M1R PAMs. This was followed by molecular descriptor analysis to detect structural similarities, utilizing molecular fingerprints (FPs) that have demonstrated effectiveness in analogous cheminformatics tasks. ref41−ref42 ref43 Finally, the most promising dual hits were subjected to structure-based virtual screening through molecular docking against both CB2R and M1R to evaluate their binding affinity and potential dual-target activity.
Collectively, this integrative and reproducible computational workflow provides a foundation and a reusable resource for the discovery of CB2R-M1R dual chemotypes. The results yield testable hypotheses and prioritized hit candidates that can inform future experimental validation and guide the subsequent design of multitarget-directed ligands relevant to the multifactorial pathophysiology of AD and related disorders.
Methods
Network Pharmacology Approach
An informatics workflow was applied to study the network pharmacology of CB2R agonists and M1R PAMs, based on the methods developed by Hajjo et al. ref44−ref45 ref46 to formulate testable hypotheses regarding the putative mechanisms of action of these modulators. This workflow consists of two major components: (1) a network-building module to identify nearest neighbor proteins that have direct interactions with CB2R and M1R; and (2) a pathway enrichment module to elucidate the biological processes involved in the mechanism of action of the chemical compounds targeting CB2R and M1R.
Protein–Protein Interactions
A systematic search for the nearest neighbor (NN) genes/proteins of CB2R and M1R was conducted in Cytoscaperef47 (version 3.10.3), using the STRINGref48 protein query application. All retrieved protein–protein interactions (PPIs), including both physical and functional interactions, were retrieved, and then, the network building tools in Cytoscape were utilized to generate PPI networks for CB2R and M1R.
Enrichment Analysis
Pathway enrichment analyses for CB2R and M1R interaction networks were performed using the STRING functional enrichment tool in Cytoscaperef47 (version 3.10.3). The significance of each enrichment result was assessed by calculating the hypergeometric p-values. Next, all ontology terms were ranked according to their calculated false discovery rates (FDRs). The FDR is a multiple testing correction method used to control the expected proportion of false positives among the list of significantly enriched pathways. Lower FDR values indicate higher statistical confidence in the enrichment result. Herein, pathways with FDR values ≤0.05 were considered statistically significant.
Generation and Curation of the CB2R and M1R Databases
Data were extracted from ChEMBL v34,ref49 based on the allocated ID for CB2R (CHEMBL253) and M1R (CHEMBL216). The database was filtered to exclusively incorporate entries that satisfied the following criteria: (1) assays were performed on human targets, and the target type was a single protein; (2) the assay description indicated that the compound exhibited either agonistic or partial agonistic effects on CB2R, or positive allosteric activity on M1R; and (3) there were no warnings in the “data validity comment” field. For entries with several records that existed for a target–ligand pair, only one assay type was retained for each compound. For compounds reported under the same assay but with different values, the mean value was calculated and used.
For CB2R, only compounds displaying EC50 ≤500 nM were retained. For M1R, compounds were retained if they displayed either an EC50 or IP ≤1000 nM or a positive log(αβ) value. As a result, 2231 compounds were identified for CB2R and 442 compounds identified for M1R. The complete data set is available in the supplementary files.
The LigPrep module in Schrödinger was used to curate the retrieved SMILES by performing salt stripping and generating both the neutral and dominant ionization states of the compounds at pH 7.4, while retaining the specified chirality and varying the undefined ones.ref50 Subsequently, canonical SMILES were generated using the RDKit Canon SMILES node available in KNIME.ref51
For docking studies, compound preparation involved LigPrep (Schrödinger) with parameters set to generate potential tautomeric and ionization states at pH 7.4 ± 0.2, employing the Epik tool in classic mode. ref50,ref52,ref53 Conformer generation was performed using the ConfGen module, with up to 64 conformations per ligand, followed by energy minimization using the OPLS4 force field. ref54,ref55
Principal Component Analysis
A principal component analysis was performed on a merged data set comprising all retrieved CB2R agonists and M1R PAMs. Prior to PCA, MACCS structural keys were generated for each compound, and pairwise Tanimoto similarity scores were computed. This analysis relied on the following Python libraries: NumPy, Matplotlib, RDKit, Scikit-learn, and StandardScaler.
Fingerprint Generation
A total of 13 distinct FPs were generated for each molecule using the CDK, ref56,ref57 RDKit,ref58 and Pybelref59 packages. Table S1 presents a concise overview of the computed FPs. This task was performed using the KNIME platform,ref60 utilizing its available FP nodes for CDK- and RDKit-based FPs. For FPs not supported in KNIME, custom Python scripts were used to generate fingerprints; the cleaned scripts and instructions are listed in the Data and Code Availability section.
Molecular Similarity Calculation
The Tanimoto coefficient (T c) similarity metric was used to compute the FP-based similarity between CB2R and M1R compounds for each of the 13 distinct FP types. Two compounds were deemed similar and selected for subsequent analysis if they met the precalculated similarity thresholds, T c0.05,ref41 in at least three different FP types.
Protein Selection and Preparation
The initial atomic coordination of CB2R-Gαi (PDB IDs: 6PT0, 3.20 Å;ref61 and 8GUR, 2.84 Åref62) and M1R-G11 (PDB ID: 6OIJ, 3.30 Åref63) were retrieved from the Protein Data Bank. A one-point mutation in M1R (Q110N3.37) (Ballesteros-Weinstein numbering in the superscript) was reverted to the wild-type sequence. The G-protein complexes were removed from both CB2R and M1R prior to applying the Protein Preparation Wizard protocol in Schrödinger, which included adding missing side chains and assigning protonation states.
Docking Sites and Molecular Docking Studies
The docking grid for the CB2R orthosteric site was generated based on the centroid of the agonist-bound active CB2R structure (WIN 55,212–2 in PDB ID: 6PT0 ref61 or CP 55,940 in PDB ID: 8GUR ref62). For the M1R allosteric site, the grid was centered on key amino acid residues previously identified in the literature as critical for PAM interactions, along with additional residues that comprehensively cover the relevant extracellular region. These residues include Y852.63, T172ECL2, Q177ECL2, Y179ECL2, L183ECL2, K392ECL3, E393ECL3, E397ECL3, W4007.35, and E4017.36. A 20 Å cubic docking grid was applied to both targets. All crystallographic water molecules were removed, and no water was retained in the docking grids. Additionally, no constraints, rotatable side chains, or excluded volumes were applied to either grid.
Schrödinger’s Induced Fit Docking (IFD) protocol was employed to accommodate receptor flexibility during ligand docking in both CB2R and M1R. For CB2R, both agonist-bound structures were used for cross-docking to explore ligand binding across two distinct ligand scaffoldsWIN 55,212–2 and CP 55,940. In the initial Glide docking step, the van der Waals scaling factor was set to 0.6 for both the receptor and the ligand. The Prime refinement stage was applied to the side chains of residues within 5 Å of the ligand. For each ligand, a maximum of 10 poses were retained for subsequent redocking in the extra precision (XP) mode.
Visualization and Figure Preparation
Schematic illustrations and graphical representations were created with BioRender (Figures fig1 and fig2) under a BioRender Publication License. Figure fig3 was generated using Cytoscape (v3.10.3),ref47 and Figure fig4 was produced with Python/Matplotlib (matplotlib.pyplot, v3.10.0).ref64 All compounds retrieved from ChEMBL were redrawn and standardized in ChemDraw prior to docking (Figures fig6 and fig8). All molecular docking visualizations and structural renders were generated using the Schrödinger Suite (Release 2025–2) (Figures fig5 , fig7, fig9, S2 and S3).
Results and Discussion
Pharmacology Network Analysis to Assess Biological Similarity
To investigate the potential biological relevance of the CB2R and M1R dual modulators, we conducted an integrated pharmacological network analysis. Initially, a PPI network was constructed using CB2R and M1R as seed nodes, expanded by their 10 nearest interacting partners using the STRING protein query tool within Cytoscape (version 3.10.3), as shown in Figure fig3 A. Functional enrichment analysis of the resulting network was performed using the STRING Enrichment app in Cytoscape, revealing several significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The top pathways included neuroactive ligand–receptor interaction, regulation of actin cytoskeleton, cholinergic synapse, phospholipase D signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, pathways in cancer, calcium signaling pathway, gastric acid secretion, and AD.
To explore a broader signaling context, the network was subsequently expanded to include 50 additional interacting proteins (Figure fig3 B). This larger network yielded enrichment of 36 KEGG pathways, with top hits again featuring neuroactive ligand–receptor interaction, cholinergic synapse, phospholipase D signaling, PI3K-Akt signaling, regulation of actin cytoskeleton, calcium signaling, Rap1 signaling, retrograde endocannabinoid signaling, and cAMP signaling. AD remained among the enriched pathways, though it was ranked 29th enriched with an FDR of 2.88 × 10–2, indicating moderate statistical significance in the broader interaction context.
Assessing the Overall Structural Similarity between CB2R and M1R Ligands
In order to assess the feasibility of designing dual ligands that interact with both CB2R and M1R receptors, it is crucial to understand how similar the ligands are between the CB2R agonists and the M1 PAM. Ligand similarity assessment helps determine how molecules interact with both receptors and their potential for cross-target binding and may even provide further insights into their functional properties. Herein, MACCS keys166-bit structural fingerprints encoding the presence or absence of predefined substructureswere generated as FPs for all ligands known to interact independently with CB2R and M1R. These fingerprints are widely used for their balance of interpretability and efficiency and have been shown to capture key structuraland potentially functionalproperties of molecules, as previously described by Hajjo et al. ref69,ref70 PCA was then performed on the MACCS key representations to explore the chemical space and assess the similarity between the two ligand groups.
The PCA plot shown in Figure fig4 provides a clear visual representation of the ligand similarities between CB2R and M1R. The clustering of ligands in the PCA plot indicates how similar (i.e., structurally related) or dissimilar (i.e., structurally distinct) the molecules are based on their MACCS FPs. Clearly, there is a good proportion of CB2R ligands that cluster together with M1R ligands in the same region of the plot. In fact, 2701 CB2R ligands spread over a large proportion of the multidimensional space formed by 166 MACCS keys and have an average Tanimoto similarity of 0.47, while the 626 M1R ligands were less dispersed, which could be due to the smaller data set size, and they had an average Tanimoto similarity of 0.58. However, the average Tanimoto similarity between the CB2R and M1R ligands was 0.45.
Thus, CB2R and M1R modulators that clustered together have similar chemical structures and, therefore, may exhibit similar binding profiles and biological activities toward both receptors. Conversely, ligands that are spread out in the plot, occupying distinct regions, suggest significant structural differences, indicating that they may interact with the designated receptors in fundamentally different ways.
Figure fig4 highlights regions of the PCA plot where the ligands from CB2R and M1R overlap and share an overall structural similarity of ≥0.80, indicated by the orange circles for CB2R ligands and magenta triangles for overlapping M1R ligands. These overlaps with high Tanimoto similarities suggest potential cross-reactivity toward both CB2R and M1R receptors. This analysis resulted in 77 CB2R ligands that share ≥0.80 Tanimoto similarity with M1R ligands and 57 M1R ligands that share ≥0.80 Tanimoto similarity with CB2R ligands (Table tbl1 ).
Multi-Fingerprint Consensus Analysis and Selectivity Filtering for Compound Prioritization
Molecular descriptors are a suitable choice for capturing similarities between compound groups, with molecular FPs being among the most widely used and effective tools in this context. For example, the Nicolotti group developed an effective approach for predicting drug targets and bioactivity using the Multifingerprint Similarity Search Algorithm. ref41−ref42 ref43 In our study, a similar consensus-based strategy was employed, incorporating 13 distinct FPs, spanning structural, circular, and topological descriptors (Table S1, Supplementary file). These FPs exhibit low intrinsic correlation with one another, indicating that each represents a unique region of chemical space.ref41 By leveraging this diversity, the approach aims to capture independent spatial dimensions of molecular similarity, thereby improving the robustness and reliability of similarity-based predictions.
Similarity values between each CB2R agonist and M1R PAM were computed using the T c. While the “0.85 Myth” posits that compounds with T c values of 0.85 or higher frequently demonstrate similar biological activity,ref71 this fixed threshold may not necessarily account for the subtle variations among FPs, as certain FPs emphasize more prevalent features, while others highlight fewer common features. To address this limitation, the Nicolotti group proposed a statistically derived similarity threshold at the 5% significance level (T c0.05), based on 10 million random pairwise comparisons from ChEMBL.ref41 Applying this criterion, 67 unique compounds from the CB2R agonist set and 33 unique compounds from the M1R PAM set met the T c0.05 threshold in at least three distinct FP types.
Subsequently, these smaller sets were re-evaluated for selectivity to ensure the absence of reported activity against known off-targets. This step aims to mitigate the risk of undesirable pharmacological events in the future. The selectivity screening was guided by data from PubChem,ref72 leading to the exclusion of promiscuous compounds with multitarget activity, particularly those exhibiting a selectivity window of less than 10-fold.ref73 Meanwhile, compounds with reported affinity for P-glycoprotein (P-gp) efflux transporters were not excluded, as they may still hold relevance for non-CNS indications, despite the study’s primary CNS focus. Consequently, 56 of the original 67 CB2R agonists and 28 of the 33 M1R PAMs were retained for further evaluation. The final selected compounds are listed in Tables S2 and S3, respectively (Supplementary file).
In Silico Investigation of Cross-Affinity Among Prioritized Compounds
CB2R-Orthosteric and M1R-PAM Binding Sites
Experimental structures of both CB2R and M1R were used to construct the in silico docking models, with known modulators serving as guides for optimizing docking parameters. To contextualize the subsequent discussion on binding analysis, key interaction patterns and residues involved in ligand recognition for each receptor are summarized below.
In the last years, the conformational space of CB2R has been experimentally elucidated in both its active and inactive states. ref61,ref62,ref74,ref75 Recently released cryo-EM structures of CB2R in complex with Gi protein demonstrate that the orthosteric binding pockettargeted by well-known agonists such as HU 308,ref62 WIN 55212–2,ref61 and CP 55,940ref62is primarily formed by residues from transmembrane helices (TMHs) 2, 3, 5, and 7 and is capped by the second extracellular loop (ECL2). Key residues involved in ligand recognition include F872.57, F912.61, F942.64, H952.65, F1063.25, and F1173.36, which engage in strong hydrophobic and aromatic interactions, along with ECL2 residues F183ECL2 and P184ECL2 that are projected into the binding crevice (Figure fig5 A). Additional residues such as T1143.33 ref62 and S2857.39 ref62,ref76 are notable for their capacity to form hydrogen bonds with a range of ligands. In contrast, inactive state structures show that antagonists need to go deeper into the highly hydrophobic pocket to stabilize the toggle switch residue W2586.48 in its inactive rotamer, thereby locking the receptor in its inactive conformation. ref74,ref76
Although no structure of the M1R in complex with a PAM is currently available, valuable insights can be drawn from the muscarinic acetylcholine receptor subtype 2 (M2R) structures in complex with an orthosteric agonist and its selective PAM LY2119620 (PDB IDs: 4MQT, 3.70 Åref25 and 6OIK, 3.60 Åref63). These data, combined with M1R site-directed mutagenesis data, ref27,ref77 molecular docking, and structure-activity relationship (SAR) analyses of M1R PAMs, ref78,ref79 suggest that M1R PAMs such as VU6007496, MK-7622, and PF-06827443 bind to an allosteric site located above the orthosteric pocket, on the extracellular surface. This binding mode is similar to that observed for M2R PAMs, with the modulators oriented toward ECL2 and ECL3.ref13
Potent M1R PAMs tend to adopt a bent three-dimensional geometry that facilitates optimal binding. In this conformation, a hydrophilic headgroup is positioned to anchor the ligand at the mouth of the extracellular vestibule via hydrogen bonds with distinctive M1R residues such as Q177ECL2, K392ECL3, and E393ECL3 (Figure fig1 C,B). A central aromatic moiety orients the polar head and separates it from the lipophilic tail, typically forming three-layer π–π stacking interactions with W4007.35 and Y179ECL2 residues. Extending from this core is a tail composed of mono- or diaryl/heteroaryl groups projected into a lipophilic pocket formed by TMHs 2 and 7. This tail reaches into an aromatic-rich region, engaging residues such as Y822.60 and Y852.63, and it may also interact with negatively charged residues like E397ECL3, another M1R-distinctive residue, potentially stabilizing a positively charged tail.
Validation of Docking Methods
To assess the accuracy and reliability of our docking methodology at the CB2R, we redocked the cocrystallized ligands from the CB2R structuresWIN 55,212–2 (PDB ID: 6PT0)ref61 and CP 55,940 (PDB ID: 8GUR)ref62 into their corresponding cryo-EM structures using IFD. The resulting poses showed root mean squared deviation (RMSD) values of 1.030 Å and 0.402 Å, respectively, relative to the experimental coordinates (Figure S1), confirming that the protocol reliably reproduces experimentally determined binding modes, thus validating its suitability for subsequent docking studies at the CB2R.
For the M1R-PAM system, where no structure is being cocrystallized with a PAM, the active-state M1R structure in complex with the orthosteric agonist Iperoxo (IXO) (PDB ID: 6OIJ)ref63 was used for docking CHEMBL4226889 (a well-characterized M1R PAM with an EC50 of 29 nMref78) at its allosteric site. The compound was selected for IFD due to its high potency and strong literature precedent. This compound guided model refinement and docking parameter optimization for subsequent studies. The resulting IFD-refined M1R-PAM complex produced a binding pose consistent with previously proposed models and known SAR data. ref27,ref77 The model accommodated the ligand with plausible binding orientations and favorable docking scores, supporting its suitability for the virtual screening of potential M1R PAM candidates.
Molecular Docking-Based Screening
For docking studies, compounds were first categorized based on shared scaffolds to streamline comparisons and facilitate interpretation of SAR within each scaffold class. Docking analysis was then guided by a combination of docking scores, published SAR data for each modulator family, and personal in cerebro insights derived from prior experience.
Docking Studies of the M1R PAMs at the CB2R Orthosteric Site
A thorough analysis of docking results for the 28 selected M1R PAMs within the orthosteric pocket of active-state CB2R revealed several compounds with favorable binding profiles, highlighting their potential as starting points for dual CB2R–M1R modulator development. Preferred binding poses were identified based on consistent interaction patterns and spatial alignment within the binding pocket.
A prominent class of active compounds features fused N-heterobicyclic aromatic scaffoldssuch as indoles, indazoles, or pyrrolopyridineswhich were notably enriched among the top hits (Compounds 1–6, Figure fig6 ). Docking studies indicated that these bicyclic systems are centrally positioned within the CB2R orthosteric pocket, anchored through π–π stacking with key aromatic clusters, including F872.57, F1173.36, F183 ECL2, and F2817.35. The five-membered ring of the cores is substituted with various para-substituted benzyl groups that extend into the cleft between TMHs 2 and 7, forming additional π–π interactions with H952.65, F183 ECL2, and F2817.35, or engaging in hydrogen bonds with L182ECL2 when bearing hydrophilic substituents. At the opposite end, the central scaffolds are linked to cyclohexyl carboxamide moieties, which adopt a stable chair conformation and protrude into the narrow channel between TMHs 3 and 5. This region allows for hydrogen bonding with T1143.33 and hydrophobic contacts with W1945.43mirroring the binding orientation of the morpholine moiety in WIN 55,212–2ref61 and the tetrahydropyran ring in LEI-102.ref62 Among this series, compounds 1, 2, 3, 5, and 6 displayed binding poses consistent with this profile (Figures fig7 A,C, S2A,B,C). In contrast, compound 4, which incorporates a bulkier hydroxymethyl-substituted cyclohexyl group, adopted an inverted binding orientation (Figure fig7 B). In this pose, the hydroxymethyl cyclohexyl moiety is oriented toward the N-term, resembling the binding mode of the cyclohexanol group in CP 55,940,ref62 while still engaging F872.57, F183ECL2, and W1945.43. This chemotype thus represents a promising scaffold for dual CB2R-M1R modulation with the potential to fine–tune activity at both receptors via strategic substitutions on the central bicyclic core.
In the case of the pyrrolopyridazine scaffold (Compounds 7–9, Figure fig6 ), the nature of the substituents on both arms affects the compound’s ability to engage the CB2R orthosteric crevice. Compounds 7 and 9 successfully expand within the binding pocket, establishing an aromatic cluster with residues from TMHs 2, 3, and 5 (Figures fig7 D and S2D). In these poses, the bicyclic arm is positioned near TMHs 2 and 7 to facilitate hydrophobic interactions with F2817.35 or F942.64, while the carboxamide headgroup is oriented toward TMHs 3 and 5, enabling hydrogen bonding with T1143.33.
Moreover, two quinoline derivativesthis time featuring fused six-membered aromatic ringsadopted binding poses reminiscent of the orientation observed for CP 55,940, likely due to their larger central core and shorter aromatic arms (Compounds 10–11, Figure fig6 ). In these poses, the hydroxycyclohexyl moiety is oriented extracellularly, positioned near TMHs 2 and 7. In compound 10, it forms a hydrogen bond with L182ECL2, while in compound 11, it interacts with S2857.39 (Figures fig7 E and S2E). The quinoline cores are stabilized through π–π stacking with F872.57 and F1173.36, and the benzyl substituents engage with F183ECL2 and W1945.43.
Similarly, the pyrazolo quinolinone scaffold holds promise as a potential dual-acting chemotype for CB2R–M1R modulation (Compounds 12–20, Figure fig6 ). This tricyclic system spans the CB2R orthosteric pocket and engages in a combination of aromatic and hydrophobic interactions with residues from TMHs 2, 3, and 5, as well as ECL2. Docking studies revealed that more branched substituents on these compounds generally orient toward the cleft between TMHs 2 and 7, while smaller substituents project toward the channel between TMHs 3 and 5 (Figures fig7 F–H and S2F–I). Some of the most favorable binders in this series are compounds 12 and 13, whose tricyclic cores display strong π–π stacking interactions with F1173.36 and F183ECL2 (Figures fig7 F and S2F). In these compounds, the fluorobenzyl (compound 12) or methylbenzyl (compound 13) substituents stack with F942.64, while the benzyl tail engages in additional stacking with F183ECL2 and W1945.43, reinforcing their anchoring within the pocket. Compound 18 similarly demonstrates extensive aromatic contacts: the tricyclic core stacks with F872.57; the pyrazolyl-benzyl arm engages F912.61, H952.65, and F183ECL2; and the benzyl group also reinforces binding via interactions with F183ECL2 (Figure fig7 H). In contrast, compounds lacking one of the arm substituentssuch as 19 and 20display poor CB2R binding. In these cases, the central scaffold is positioned too high within the binding crevice, failing to establish the critical anchoring interactions necessary for stable binding.
On the other hand, compounds bearing a quinolizinone corerepresented by M1R allosteric modulators 21 to 26exhibited limited ability to fit inside the CB2R (Figure fig6 ). These molecules possess highly branched architectures that prevent effective engagement of the central aromatic core with the hydrophobic orthosteric pocket of CB2R. As a result, they display overall poor shape complementarity and suboptimal interaction patterns, leading to a reduced binding potential.
While compounds 27 and 28featuring thienopyridinone and indolinedione scaffolds, respectivelywere found to be too small to effectively occupy the key subpockets of the CB2R binding site, further limiting their binding potential (Figure fig6 ).
Docking Studies of the CB2R Agonists at the M1R Allosteric Site
Structural analysis of docking studies for the 56 selected CB2R modulators revealed several compounds with key structural characteristics compatible with the M1R allosteric binding site, indicating their potential as dual CB2R-M1R modulators.
Among these, compound 29, featuring an oxoindolinylidene benzohydrazide scaffold, showed promising results due to its optimal bent conformation, which fits snugly within the M1R PAM binding site (Compound 29, Figures fig8 and fig9A). Docking studies indicated that the oxoindolinylidene moiety aligns parallel to W4007.35, enabling aromatic stacking; the benzohydrazide carbonyl’s proximity to K392ECL3 facilitates an ion–dipole interaction, and its benzene ring engages in π–π stacking with Y179ECL2. Concurrently, the benzyl tail projects toward the aromatic-rich region between TMHs 2 and 7, forming additional stacking interactions that further stabilize the binding pose.
A second promising scaffold class includes the cyclopentapyrazole carboxamides, which were particularly abundant and featured structural variations at the carboxamide cap (Compounds 30–35, Figure fig8 ). Remarkably, the R,R-cyclopropyl isomers demonstrated superior accessibility and fit within the M1R PAM pocket, suggesting a favorable conformational bias. In this series, the pyrazole cores are stacked with W4007.35, which is applicable only when the cyclopropyl group is oriented away from this residue in the R,R isomers. The carboxamide head groups extended toward the extracellular vestibule and could form key hydrogen bonds with Q177ECL2, K392ECL3, and/or D393ECL3, and in some cases engage in π-cation interactions with K392ECL3 depending on the nature of the cap. Meanwhile, the difluorophenyl moiety further stabilizes the pose via stacking not only with Y852.63 but also with Y4047.39, the latter being a residue known for its role in π-cation interactions with orthosteric agonists and potentially contributing to receptor stabilization. Among this series, compounds 30–33 emerged as the most promising derivatives (Figures fig9 B,C and S3A,B , respectively). In contrast, compounds featuring bulky noncyclic caps (e.g., compound 34) or lacking branched polar groups on their cyclic caps (e.g., compound 35) failed to establish effective interactions. Overall, the structural diversity at the cap positions provides a compelling foundation for systematic SAR exploration and offers strong potential for fine-tuning dual activity toward the development of optimized modulators.
Indole and pyrrolopyridine carboxamide derivatives also showed promising characteristics (Compounds 36–38, Figure fig8 ). These compounds incorporate an additional carboxamide group at the head moiety, paired with a benzylic substituent, permitting extra hydrogen bonds and potential π-cation interactions with ECL2 or ECL3 residues. Docking indicated that the indole or pyrrolopyridine carboxamide promotes aromatic stacking with W4007.35 and/or Y179ECL2, along with hydrogen bonding with Q177ECL2 (Figures S3C,D and fig9D, respectively). The appended benzyl carboxamide could further contribute by forming additional hydrogen bonds with Q177ECL2, E393ECL3, and/or K392ECL3. Despite the fact that these derivatives have hydrocarbon tails rather than rigid aromatic systems, docking results suggested that the aliphatic chains are accommodated within the hydrophobic pocket between TMHs 2 and 7. This compatibility highlights their good potential for further structural optimization.
Due to their strong structural resemblance to well-known M1R PAMs, especially compound 39, the naphthyridinone carboxamide scaffold stood out as one of the most promising dual modulators (Compounds 39–41, Figure fig8 ). Its bent conformation enabled the bicycle core to engage in π–π stacking with W4007.35 and/or Y179ECL2, while the carbonyl oxygens formed hydrogen bonds with K392ECL3 and Y852.63, and the aromatic tail extended toward the TMHs 2 and 7 region (Figure fig9 E). Derivatives lacking aromatic tails, such as compounds 40 and 41, still displayed fair binding potential, where the former showed more favorable hydrogen bond interactions than did the latter compound (Figure S3E,F, respectively).
Similarly, a prominent core shared by M1R PAMs and CB2R agonists is the pyrazoloquinolinone scaffold. The primary differences lie in the tail region: M1R-directed analogs possess aromatic tails (compounds 12–20, Figure fig6 ), whereas CB2R counterparts feature flexible pentyl chains (compounds 42–45, Figure fig8 ). Docking studies revealed that compounds 42 and 43 engage in key interactions, including aromatic stacking with W4007.35 and/or Y179ECL2, hydrogen bonding with Q177ECL2, and favorable orientation of the pentyl tail within the TMHs 2 and 7 hydrophobic pocket (Figures fig9 F and S3G, respectively). Conversely, compounds 44 and 45, which incorporate an extended linker and a bulky adamantanyl head, respectively, display significantly lower interaction quality, likely due to steric hindrance disrupting optimal positioning within the binding site.
The pyrid-4-one carboxamide scaffold was highly represented (compounds 46–56, Figure fig8 ). Top binders, such as compounds 46–48, combine cyclohexyl or tetralin heads with a 6-phenyl substitution. These compounds effectively engaged in stacking with W4007.35 and/or Y179ECL2 while simultaneously forming hydrogen bonds with K392ECL3 and/or Q177ECL2 without encountering steric hindrance (Figures fig9 G and S3H,I, respectively). Despite the fact that these derivatives possess monocyclic aromatic cores, the 6-phenyl group seems to compensate by permitting additional stacking. Superposition of these ligand docking poses with those of bi/tricyclic analog docking showed favorable spatial overlaps, suggesting that the phenyl group is well accommodated when oriented orthogonally to the pyridone ring. Nevertheless, their flexible hydrocarbon tails may limit the binding efficacy by favoring van der Waals interactions over robust aromatic stacking. While derivatives featuring adamantanyl or adamantanyl-methyl head groups or branched substituents at position 6 were too bulky and lacked compatibility with the pocket.
Similarly, quinolinone carboxamide derivatives (Compounds 57–60, Figure fig8 ), featuring a fused bicyclic core this time, were evaluated for binding. Among them, only compound 60, which incorporates a tetralin headgroup, showed a favorable binding profile, outperforming analogs bearing bulkier headgroups (Figure fig9 H).
Another scaffold was the indole carboxamide, characterized by a single carboxamide group (Compounds 61–69, Figure fig8 ), in contrast to the dicarboxamide structure seen in compound 36. Notably, derivatives bearing a naphthyl or cyclohexyl substituent at the carboxamide group exhibited the best fit within the binding site, striking an intriguing pose reminiscent of the selective M2R PAM LY2119620, as observed in M2R–IXO–PAM complex structures (PDB: 4MQT ref25 and 6OIK ref63). In this orientation, the indole core is preserved for π–π stacking with W4007.35 and Y179ECL2, while the carboxamide moiety is directed toward the TMHs 2 and 7 region this time (Figures fig9 I and S3J). Here, the carbonyl oxygen forms a hydrogen bond with Y822.60, and the amide nitrogen is positioned to interact with E397ECL3, potentially via a water-mediated hydrogen bond. Additionally, the naphthalene ring in compound 61 further stabilized the binding via aromatic stacking with Y852.63. While other derivatives that possess a bulky adamantanyl headgroup were unable to fit within the binding site and demonstrated limited binding.
A similar binding orientation was observed for the indolyl-phenyl methanone derivatives (Compounds 70–71, Figure fig8 ). Within this series, compound 70 outperformed compound 71, likely due to the presence of a hydrophilic N-substituent in compound 70 that extended toward ECL3, enabling an extra ion–dipole interaction with K392ECL3 (Figures fig9 J and S3K, respectively). Nonetheless, while these results are encouraging, further validation will be necessary to validate the proposed binding modes for both scaffold classes.
Conversely, several scaffold series demonstrated poor suitability for the M1R PAM site due to insufficient size, overly short structures, or excessive steric bulk. The dicyclohexylpicolinamide derivatives (Compounds 72–73, Figure fig8 ), featuring monocyclic aromatic cores and short cyclohexyl tails, result in shallow surface-level binding with weak interaction profiles. Similarly, the adamantanyl indolinyl or quinolinyl methanone derivatives (Compounds 74–76, Figure fig8 ) were unable to form critical contacts due to their diminutive size and the steric impediment created by the bulky adamantanyl group. Their structural resemblance to dopaminergic modulators may also raise concerns about off-target effects. The pyrazole carboxamide scaffold (Compound 77, Figure fig8 ), represented by a single Y-shaped molecule bearing a bulky trimethylbicycloheptanyl head, was excessively sterically hindered, preventing effective access to the binding pocket and precluding the formation of critical hydrogen bonds. Lastly, pyrid-2-one carboxamide derivatives (Compounds 78–80, Figure fig8 ) suffered from steric congestion, particularly in the tetrasubstituted analogue, leading to poor accommodation within the M1R PAM site.
Conclusion
Our multistrategy computational pipeline for MTDL candidate selection establishes a strong basis for addressing the discovery of dual CB2R-M1R modulators. Pharmacological network analysis not only supported the potential relevance of such dual modulators in AD–related pathways but also revealed their broader multitarget potential for application in associated neurological illnesses. Prior studies have demonstrated the synergistic benefits of cotargeting the endocannabinoid and cholinergic systems in AD. ref33,ref80,ref81 For example, one research team developed hybrid dual-acting compounds that both inhibit butyrylcholinesterase (BChE) while activating CB2R,ref82 and another pursued a molecule combining acetylcholinesterase (AChE)/BChE inhibition with CB2R modulation.ref83 Both strategies yielded compounds with neuroprotective effects in vitro and demonstrated cognitive improvement in vivo.
Compared with these prior strategies, our approach simultaneously targets two GPCRsCB2R and M1Rboth embedded in the lipid bilayer, offering the advantage that receptors share similar membrane-associated localization, signal transduction mechanisms, and drug-accessibility properties. Such spatial and functional alignment increases the likelihood of achieving comparable pharmacokinetic and pharmacodynamic profiles within a single chemotype, in contrast to the challenges often encountered when dual ligands combine mechanistically unrelated targets, such as receptors and enzymes. Importantly, the design is based on a single chemotype capable of modulating both receptors rather than relying on hybridization or linkage of separate pharmacophores. While previous hybrid ligands, including CB2R–cholinesterase ref82,ref83 and CB2R–FAAHref67 duals, have demonstrated therapeutic promise, they often display suboptimal drug-likeness due to their bulky scaffolds, elevated molecular weight, excessive lipophilicity, and limited brain penetration.
Moreover, while cholinesterase inhibitors act indirectly to increase acetylcholine and are frequently associated with peripheral side effects like gastrointestinal and hepatotoxicity,ref84 M1R PAM directly stimulates central cholinergic signaling pathways with enhanced specificity for cognition-related circuits in the CNS. Collectively, this dual GPCR approach has the potential to deliver improved drug-likeness, greater brain penetration, enhanced safety, and a more precise disease-modifying impact in neurodegenerative disorders.
Building upon these insights, our approach can also be viewed as a computational repurposing strategy, in which compounds previously validated for activity on one receptor undergo systematic evaluation and optimization to extend their utility for a secondary, mechanistically related target. This strategy leverages existing knowledge of bioactivity to enhance the reliability of predicted interactions, mitigate safety concerns during subsequent experimental validation, and optimize the utilization of known chemical entities. This approach provides a logical and resource-efficient method for identifying new MTDLs with improved translational feasibility.
PCA of MACCS FBs revealed significant chemical overlaps between CB2R agonists and M1R PAMs, supporting the feasibility of identifying dual-target candidates. The subsequent integration of diverse molecular descriptor profiling and comprehensive docking analysis further enabled the efficient shortlisting of promising dual modulators. Scaffold analysis identified chemotypes with structural features suitable for engaging both binding sites. Notably, several CB2R agonists and M1R PAMs share core structures such as pyrazoloquinolinone (Figure fig6 : 12–18; Figure fig8 : 42–43) and indole carboxamide (Figure fig6 : 1–2; Figure fig8 : 61–62), highlighting their suitability for future pharmacophore modeling and SAR-guided optimization.
Collectively, these findings underscore the value of integrative computational strategiescombining pharmacological profiling, molecular descriptor analysis, and structure-based modelingin the rational design of MTDL relevant to neurodegenerative disorders. The generated results offer testable, hypothesis-driven insights that serve as evidence-based computational resources to guide experimental scientists, thereby contributing to bridging the gap between computational discovery and experimental validation. We acknowledge that the present work is entirely computational in nature; therefore, experimental confirmation through in vitro binding assays, functional cellular studies, and in vivo evaluations in AD models will be crucial next steps to verify and refine these predictions. Accordingly, we outline a clear roadmap for future studies aimed at validating and expanding upon these computational findings.
Supplementary Material
Acknowledgements
The authors would like to express their sincere gratitude and appreciation for the opportunity to use the UNC research computing cluster at The University of North Carolina at Chapel Hill. The Table of Contents (TOC) graphic was created with BioRender. Isawi, I. (2026) https://BioRender.com/15n0uqd Generative AI (OpenAI) was used to assist with code cleanup/refactoring (e.g., improving readability and formatting). All changes were reviewed by the authors, and the final scripts were validated through execution and reproducibility checks.
Notes
Cleaned executable scripts and documentation are available at: https://github.com/ihisawi/Custom-Python-scripts-for-generating-Fingerprints.git
Notes
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c07866.
- Fingerprint notations along with the open-source software packages used for their calculation; chemical structures of M1R PAMs selected to investigate their potential cross-affinity with CB2R using IFD; chemical structures of CB2R agonist compounds selected to investigate their potential cross-affinity with M1R using IFD; superimposition of docked and cocrystallized poses of CB2R agonists; key interactions between the M1R PAMs docked within the CB2R orthosteric binding pocket; and key interactions between the CB2R agonists docked within the M1R allosteric binding pocket (PDF)
- Cleaned data of CB2R agonists and M1R PAMs (XLSX)
Notes
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
Notes
This work was funded by the Deanship of Scientific Research at Jordan University of Science and Technology under Grant Number 20220048.
Notes
Ethical approval: This study does not contain any studies involving humans, animals, plants, biological material, protected or nonpublic data sets, collections, or sites.
Notes
The authors declare no competing financial interest.
Glossary
- CB2R
- cannabinoid receptor type 2
- M1R
- muscarinic acetylcholine receptor subtype 1
- MTDLs
- multitarget directed ligands
- GPCRs
- G-protein coupled receptors
- AD
- Alzheimer’s Disease
- PAMs
- positive allosteric modulators
- FPs
- fingerprints
- N,N
- nearest neighbor
- PPIs
- protein–protein interactions
- FDRs
- false discovery rates
- PCA
- principal component analysis
- Tc
- Tanimoto coefficients
- IFD
- induced fit docking
- XP
- extra precision
- KEGG
- Kyoto Encyclopedia of Genes and Genomes
- TMHs
- transmembrane helices
- ECL
- Extracellular loop
- SAR
- structure-activity relationship
- IXO
- Iperoxo