Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery

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Methods Here, we identified novel CB1 agonists using virtual screening approaches. First, we developed a pharmacophore model based on the known CB1 agonist AM11542 and screened a database of over three million compounds. Molecular docking using AutoDock Vina identified 61 hits with binding affinities of less than -9.00 Kcal/mol. Subsequent ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) analysis narrowed the selection to 18 promising candidates. Results Among these, three agonists exhibited strong characteristics, including a favorable inhibition constant (Ki) and key hydrogen-bond interactions with critical residues in the CB1 binding pocket: PUBChem157251136 (Ki=2.09 nM), ZINC64438485 (Ki= 0.262 nM) and ZINC64438506 (Ki =0.244 nM). These agonists formed stable hydrogen bonds with CB1 binding pocket residues (Ser383, Ser173, His178 and Thr197). Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. These comprehensive simulations confirm that all identified agonist complexes maintain stable binding conformations with optimal interaction profiles characteristic of CB1 receptor activation. Conclusion These results could pave the way for researching and developing new quinazolinz-2, 4(1H, 3H)-Dione derivatives as a new class of CB1 receptor agonists. 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F1000Research 2026, 14 :1322 ( https://doi.org/10.12688/f1000research.171433.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] Abdellah EL AISSOUQ https://orcid.org/0000-0001-6909-9828 1 , MOURAD STITOU 1 , Mohamed Enneiymy 2 , [...] Said El Rhabori 1 , Hicham Zaitan 1 , Abdelkrim Ouammou 3 , Fouad Khalil 1 Abdellah EL AISSOUQ https://orcid.org/0000-0001-6909-9828 1 , MOURAD STITOU 1 , [...] Mohamed Enneiymy 2 , Said El Rhabori 1 , Hicham Zaitan 1 , Abdelkrim Ouammou 3 , Fouad Khalil 1 PUBLISHED 06 Jan 2026 Author details Author details 1 Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Fes-Boulemane, Morocco 2 Universite Ibn Zohr Faculte des Sciences Agadir, Agadir, Souss-Massa-Draa, Morocco 3 Universite Sidi Mohamed Ben Abdellah Faculte des Sciences Dhar El Mahraz-Fes, Fes, Fes-Boulemane, Morocco Abdellah EL AISSOUQ Roles: Methodology, Writing – Original Draft Preparation, Writing – Review & Editing MOURAD STITOU Roles: Investigation, Methodology, Validation, Visualization Mohamed Enneiymy Roles: Methodology, Supervision, Validation, Visualization Said El Rhabori Roles: Methodology, Resources, Software, Validation Hicham Zaitan Roles: Data Curation, Supervision, Validation, Visualization Abdelkrim Ouammou Roles: Data Curation, Investigation, Methodology, Validation Fouad Khalil Roles: Methodology, Supervision, Validation, Visualization OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Cheminformatics gateway. Abstract Background The cannabinoid 1 (CB1) receptor is the primary target of Δ 9 -tetrahydrocannabinol (Δ 9 -THC), the psychoactive component of cannabis sativa (commonly known as “kif” in Morocco). Methods Here, we identified novel CB1 agonists using virtual screening approaches. First, we developed a pharmacophore model based on the known CB1 agonist AM11542 and screened a database of over three million compounds. Molecular docking using AutoDock Vina identified 61 hits with binding affinities of less than -9.00 Kcal/mol. Subsequent ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) analysis narrowed the selection to 18 promising candidates. Results Among these, three agonists exhibited strong characteristics, including a favorable inhibition constant (Ki) and key hydrogen-bond interactions with critical residues in the CB1 binding pocket: PUBChem157251136 (Ki=2.09 nM), ZINC64438485 (Ki= 0.262 nM) and ZINC64438506 (Ki =0.244 nM). These agonists formed stable hydrogen bonds with CB1 binding pocket residues (Ser383, Ser173, His178 and Thr197). Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. These comprehensive simulations confirm that all identified agonist complexes maintain stable binding conformations with optimal interaction profiles characteristic of CB1 receptor activation. Conclusion These results could pave the way for researching and developing new quinazolinz-2, 4(1H, 3H)-Dione derivatives as a new class of CB1 receptor agonists. READ ALL READ LESS Keywords CB1 agonists, pharmacophore based virtual screening, lead discovery, quinazoline-2,4(1H,3H)-dione derivatives Corresponding Author(s) Abdellah EL AISSOUQ ( [email protected] ) Close Corresponding author: Abdellah EL AISSOUQ Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 EL AISSOUQ A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: EL AISSOUQ A, STITOU M, Enneiymy M et al. Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.12688/f1000research.171433.2 ) First published: 27 Nov 2025, 14 :1322 ( https://doi.org/10.12688/f1000research.171433.1 ) Latest published: 11 Mar 2026, 14 :1322 ( https://doi.org/10.12688/f1000research.171433.3 ) Revised Amendments from Version 1 Title: The original title " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " has been changed to " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery ". Introduction section: Ligne 21: The abbreviation GPCRs has been clarified by adding “G Protein-Coupled Receptors (GPCRs) ”. Ligne 43: The abbreviation ICL3 has been clarified by adding “ three intracellular loops (ICL3 )”. Different synthesis routes of substituted quinazoline-2,4-dione scaffold section: Page 27, line 13: "Translated with DeepL.com (free version)" has been removed. Title: The original title " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " has been changed to " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery ". Introduction section: Ligne 21: The abbreviation GPCRs has been clarified by adding “G Protein-Coupled Receptors (GPCRs) ”. Ligne 43: The abbreviation ICL3 has been clarified by adding “ three intracellular loops (ICL3 )”. Different synthesis routes of substituted quinazoline-2,4-dione scaffold section: Page 27, line 13: "Translated with DeepL.com (free version)" has been removed. See the authors' detailed response to the review by Samir Chtita and Bouchra Rossafi See the authors' detailed response to the review by Fatima En-nahli See the authors' detailed response to the review by Mourad Fawzi READ REVIEWER RESPONSES  There is a newer version of this article available. Suppress this message for one day. I. Introduction Cannabinoid receptors are classified as G protein-coupled receptors which belong to a family known as the endocannabinoid system, a master regulator of numerous physiological pathways throughout the human body. 1 There are two categories of cannabinoid receptors, known as CB1 and CB2. The CB1 receptor is found throughout multiple organs of the body; it is present in the digestive tract, liver, pancreas, and musculature, in addition to its primary site of localization being in the brain. 2 In fact, CB1 is the most highly expressed receptor in the brain relative to other receptors examined, possessing 7 transmembrane domains with G protein coupling. The CB1 receptor mediates cannabinoid-induced psychotropic effects. The CB2 receptor is more so located within immune cells, where it exerts its immunomodulatory role. Cannabinoids are a class of chemical compounds which are championed globally for their psychoactive and physiologic whole body functions; for at least 5,000 years, 3 , 4 mankind has been able to capitalize on the value of cannabinoids. One such natural source of cannabinoids is cannabis, known as “kif” in Moroccan culture, a natural product with a plethora of phytoconstituents including Δ9-tetrahydrocannabinol (THC). The phytochemical THC exerts its psychotropic effects by acting at the CB1 receptor, which also serves as the primary receptor for the endogenous endocannabinoids anandamide (AEA) and 2 arachidonoylglycerol (2-AG). 5 Activation of the CB1 receptor upregulates potassium channel currents and calcium ion channel polarization, making receptor signaling dose dependent and responsive to treatment with pertussis toxin. 6 In addition, CB1 can exist as a homodimer and/or form complexes as heterodimers or heterooligomers with other G Protein-Coupled Receptors (GPCRs). Lastly, the CB1 receptor is in a complex with GABAergic and glutamatergic cells, and therefore, CB1 receptor stimulation decreases release from GABAergic and glutamatergic cells. 7 The structural complexes of the cannabinoid receptor CB1 with THC analogs are an important topic of research to help us understand the molecular interactions that underlie cannabinoid signaling. Thus far, the repertoire of known complexes of the CB1 receptor includes the structure of CB1, bound to AM8411, 8 CP55940 9 and AM11542 10 ( Figure 1 ). These structures have a resolution of 2.8-3.4 Å and provide a highly informative account of the binding modes and associated conformational changes that can accompany receptor activation. Several important interacting residues have been found to be conserved between agonist-bound structures, including hydrophobic interactions with LEU193 3.29 , VAL196 3.32 , PHE200 3.36 , TYR275 5.39 , LEU276 5.40 , TRP279 5.43 , TRP356 6.48 , LEU359 6.51 and MET363 6.55 . 11 These observations further emphasize the importance of these residues in their role of stabilizing binding of agonists to the receptor, while also promoting receptor activation. Figure 1. CB1 receptor agonist chemical structures. There were also three structures solved by means of cryo-electron microscopy (cryo-EM) that included full active states along with the Gi1-2 G protein subunit. 12 The AM11542 CB1 complex is a crystalline structure and had a fusion protein, flavodoxin, which contributes to stabilization of TM6 in an active confirmation. 11 All of the structural coordinates cover the full CB1 protein sequence, covering anywhere between approximately 58% and 62% of each residues in all active structures. 8 The fact that the coordinates do not cover the full sequence is most likely do to the flexibility imparted by both the N and C-terminus and the long three intracellular loops (ICL3). These structural descriptions not only allow constructs for determining the molecular mechanisms for THC analog activation of the CB1 receptor but also create opportunities for future studies. In recent years the harmonization of approaches have created several avenues to create CB1 receptor agonists with less adverse effects. This study will previously develop a CB1 agonist pharmacophore model that was used as a virtual screening tool for unique/novel class of CB1 agonists. Using the pharmacophore this was able to screen over 300 million hits, of compounds, parsed from 11 different databases and were able to identify many candidates that have not been screen for cannabinoid receptor binding assay. This highlights the usefulness of computational strategies to find novel therapeutics that have the potential to provide safer alternatives. II. Materials and method 1. Pharmacophore modeling and virtual screening Pharmacophore based virtual screening were performed using the Pharmit web interface ( http://pharmit.csb.pitt.edu/ ), which has a number of built-in databases that provide access to comprehensive data; Molprot (4,742,020 molecules), ChEMBL34 (2,264,112 molecules), ZINC (13,127,550 molecules), ChemDiv (1,456,120 molecules), ChemSpace (50,181,678 molecules), Enamine (4,117,328 molecules), MCULE (39,843,637 molecule), MCULE-ULTIMATE (126,471,502 molecules), NCI Open Chemical Repository (52,237 molecules), LabNetwork (1,794,286 molecules), and PubChem (103,302,052 molecules). The pharmacophore model was constructed using selected PDB code 5XRA from the RCSB Protein Data Bank ( http://www.rcsb.org/structure/5xra ), with agonist AM11542. The model utilized a pharmacophore framework built on five features, adhering to the default parameters of the Pharmit server. The Pharmit filters were applied based on the Lipinski rule of five and Veber’s rule to refine the screening process and identify the most selective CB1 agonist. Figure 2 illustrates the multi-step virtual screening process used in this work. Figure 2. General workflow used in the present study for identifying new CB1 agonists. 2. ADMET profiling The ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) filter is one of the phases of the drug discovery and development process. 13 – 15 This allows researchers to determine potentially druggable drug-like properties and toxicology early on to assess which compounds will be effective drugs with safety potential. Thus, relative to virtual screening, such a filter can detect adverse properties that would fail at much later stages, such as insufficient absorption, excessive toxicity, and/or suboptimal bioavailability. Two of the trusted websites relied upon to evaluate such properties based upon lipophilicity, logP, solubility, etc., are SwissADME ( http://www.swissadme.ch ) and pKCSM ( https://biosig.lab.uq.edu.au/pkcsm/prediction ). Therefore, when these are filtered out early on, time and costs will be effectively saved down the line. 3. Molecular docking studies To estimate the binding affinities of the 433 highest-ranked compounds to CB1, molecular docking was carried out using AutoDock Vina integrated within the PyRx platform. The crystal structure of CB1 (PDB ID: 5XRA), with a resolution of 2.8 Å, 8 , 16 , 17 was retrieved from the Protein Data Bank. Before docking, water molecules and the native ligand were removed from the protein, followed by the addition of polar hydrogen atoms and Kollman charges. The prepared protein structure was then energy-minimized using UCSF Chimera and saved in PDBQT format. Prior to docking, all 433 compounds underwent pre-optimization utilizing the Universal Force Field (UFF) in combination with the conjugate gradient algorithm. 18 Optimization parameters were set to 2000 total steps, with updates occurring every step, and the process was programmed to terminate once the energy difference fell below 0.01 kcal/mol. 19 After optimization, the compounds were converted to PDBQT format and docked at specific binding site coordinates (x = -42.052, y = -164.338, z = 306.631). Molecules demonstrating the lowest binding energies and root-mean-square deviation (RMSD) values below 2 Å were selected for subsequent analyses. 4. Molecular dynamics simulations Molecular dynamics (MD) simulations were carried out on the top-ranked docking conformations using the GROMACS 2024.4 software package, employing the CHARMM27 all-atom force field. 20 The topology files for the CB1 receptor were generated using the pdb2gmx tool, 21 while the ligand topologies were prepared through the CHARMM General Force Field (CGenFF) using the Param-Chem server. 22 – 24 Each receptor-ligand complex was embedded in a triclinic simulation box and solvated with TIP3P water molecules, maintaining a minimum distance of 1.0 nm from the box boundaries. To neutralize the systems, Na + and Cl - ions were added accordingly. Prior to the production runs, energy minimization was performed using the steepest descent algorithm for 50,000 steps to eliminate steric clashes and ensure system stability. Subsequently, two equilibration phases were conducted: first under the NVT ensemble to stabilize temperature, followed by the NPT ensemble to equilibrate pressure and density. Input files for both equilibration and production stages were customized to adjust parameters such as trajectory-saving intervals, energy monitoring, and other essential simulation settings. Finally, each ligand-receptor system underwent 100 ns of production MD simulation at a constant temperature of 300 K, pressure of 1 bar, and a time step of 2 femtoseconds. 5. MM/GBSA method The binding free energy was calculated via MM/GBSA (Molecular Mechanics Generalized Born Surface Area). 25 This approach utilizes force fields of molecular mechanics and an implicit solvent approach to conclude stability and binding tendencies of molecular complexes. It is particularly useful for classifying ligands and understanding overall energetic contributions of molecular recognition. The binding free energy ΔGbind of a complex can be calculated as follows: (Eq.1) ΔG bind = ΔE MM + ΔG solv – TΔS Where: (Eq.2) ΔE MM = E MM complex − ( E MM receptor + E MM ligand ) (Eq.3) ΔG solv = G solv complex − ( G solv receptor + G solv ligand ) G solv = G GB + G SA Where TΔS, ΔE MM and ΔG sol are the conformational entropy upon binding, the changes of the gas phase molecular mechanism (MM) energy and the solvation free energy, respectively. III. Results and discussion 1. Pharmacophore The pharmacophore model of the CB1 receptor agonist was generated based on the AM11542 agonist. The results are shown in Figure 3 . A five-point model with 1 aromatic, 2 hydrogen bond acceptors and 2 hydrophobic regions was generated using the Pharmit web server. 26 Figure 3. Pharmacophore features (a) two hydrogen bond acceptors (2HBA, yellow color), two hydrophobic groups (2HY, Green color), and one aromatic group (1AR, purple color), (b) Pharmacophore feature mapping of AM11542 agonist. Subsequently, virtual screening was performed across 11 databases, identifying 433 compounds containing molecular groups that matched the pharmacophore mode. 2. Virtual screening Pharmacophore-based virtual screening was performed using information from the previous pharmacophore model. Approximately 300 million compounds from the Pharmit database were filtered. The criteria for filtering the library were as follows: molecular weight limits were set at 250-500 Da, the maximum number of hydrogen bond donors was less than 4, the maximum number of hydrogen bond acceptors was less than 9, the maximum number of rotatable bonds was less than 9, Log P was between 2 and 5, and polar surface area was less than 140 Å. The results are computed and classified according to different criteria such as energy minimization. The 433 top-ranked compounds are presented in Table 1 . Table 1. Pharmacophore-based virtual screening of compounds from 11 databases on the Pharmit server. Pharmit database Molecules Hits Molprot 4,742,020 58 ChEMBL34 2,264,112 134 ZINC 13,127,550 32 ChemDiv 1,456,120 76 ChemSpace 50,181,678 0 Enamine 4,117,328 5 MCULE 39,843,637 23 MCULE-ULTIMATE 126,471,502 0 NCI Open Chemical Repository 52,237 0 LabNetwork 1,794,286 63 PubChem 103,302,052 42 Total 347.352.522 433 Next, molecular docking analysis was performed using AutoDock Vina, implemented in the PyRx software. Compounds with a binding affinity of less than - 9.00 kcal/mol were selected for further analysis, while the others were excluded. Additionally, the predicted binding modes were required to have a root-mean-square deviation (RMSD) of less than 2Å when superimposed onto the native agonist (AM11542). The top 61 selected compounds are listed in Table 2 , while the remaining candidates are provided in Table S1 (see supplementary data). Table 2. Top 61 compounds selected based on docking binding affinity (binding affinity < -9.00 Kcal/mol) and rmsd (rmsd < 2 Å) values. Code Binding affinity (Kcal/mol) rmsd Code Binding affinity (Kcal/mol) rmsd ZINC35377792 -10.526 1.766 ZINC21525754 -9.349 1.086 ZINC17286185 -10.487 1.825 ZINC35377780 -9.311 1.265 ZINC45898833 -10.415 1.964 PubChem-16352732 -9.308 0.823 ZINC35562518 -10.410 1.055 PubChem-136659176 -9.294 1.082 ZINC35377809 -10.202 1.239 PubChem-135405892 -9.292 1.457 ZINC64438506 -10.189 1.395 PubChem-126853168 -9.271 1.275 PubChem-156469643 -10.074 1.867 PubChem-25220434 -9.254 1.924 PubChem-89734004 -10.054 1.291 ZINC20138980 -9.240 1.274 PubChem-50762870 -10.049 1.717 ZINC169 -9.236 1.227 PubChem-86747888 -10.035 1.776 ZINC96385691 -9.222 1.955 ZINC45899482 -10.006 1.796 ZINC65196494 -9.217 0.750 ZINC64438485 -9.944 1.964 MolPort-009-386-250 -9.208 1.702 PubChem-121231416 -9.858 1.197 PubChem-41119771 -9.192 1.542 ZINC21797190 -9.850 1.617 ZINC20113894 -9.173 0.794 ZINC45899726 -9.828 1.718 ZINC20113894 -9.173 0.794 ZINC45900106 -9.824 1.499 ZINC35377767 -9.130 1.670 PubChem-53794837 -9.820 1.352 PubChem-71600230 -9.127 1.653 ZINC45899547 -9.785 1.651 ZINC45899386 -9.127 1.416 CHEMBL1652254 -9.763 1.547 ZINC35377767 -9.121 1.351 ZINC2245716 -9.652 1.160 ZINC13365292 -9.100 1.219 ZINC45900103 -9.615 1.977 LN00379431 -9.098 1.083 ZINC35377763 -9.518 1.176 PubChem-3750748 -9.095 1.627 PUBChem 157251136 -9.492 1.362 ZINC4034881 -9.089 1.300 ZINC09598984 -9.489 1.654 ZINC65196500 -9.068 1.934 ZINC35377731 -9.485 1.417 ZINC100771598 -9.068 1.445 ZINC 35377767 -9.465 1.474 PubChem-25352696 -9.067 1.172 ZINC 21723065 -9.463 1.471 PubChem-91428044 -9.045 1.614 PubChem-42810820 -9.454 1.742 ZINC35377731 -9.034 1.382 ChemDiv-C260-2692 -9.424 1.461 ZINC33057775 -9.027 1.058 PubChem-135869534 -9.413 1.985 ZINC96385592 -9.010 1.069 PubChem-91487881 -9.353 1.714 3. Toxicity filters The ranked compounds from docking analysis were evaluated for potential toxicity, including an AMES toxicity test, an acute oral toxicity test in rats (LD 50 ), a skin sensitization test and a maximum tolerated dose analysis. Highly toxic compounds are not considered in further studies. The top selected compounds are presented in Table 3 . The other are presented in the supplementary data ( Table S2). Table 3. In silico prediction of Skin Sensitisation. AMES toxicity, oral rat acute toxicity (LD 50 ), and Max Tolerated dose in humans. Code Skin Sensitisation AMES toxicity Oral Rat Acute Toxicity (LD 50 (mol/kg)) Max. tolerated dose (human) (Log mg/kg/day) ZINC35377792 No No 2.785 0.412 ZINC17286185 No No 2.474 0.222 ZINC45898833 No No 2.617 0.328 ZINC35562518 No No 2.441 0.215 ZINC64438506 No No 2.744 0.685 PubChem-156469643 No No 2.904 -1.12 PubChem-89734004 No No 3.02 -0.825 PubChem-86747888 No No 3.001 -0.913 ZINC64438485 No No 2.816 0.381 PubChem-121231416 No No 2.818 -0.595 ZINC21797190 No No 3.085 0.61 ZINC45899726 No No 2.748 0.647 PubChem-53794837 No No 2.569 -0.725 CHEMBL1652254 No No 3.004 0.428 ZINC2245716 No No 2.469 0.586 PUBChem 157251136 No No 2.812 -0.095 Zinc35377731 No No 2.863 0.328 Zinc21723065 No No 2.544 0.463 PubChem-42810820 No No 2.73 0.562 ChemDiv-C260-2692 No No 2.475 0.6 PubChem-135869534 No No 3.077 0.604 PubChem-91487881 No No 2.433 -0.088 ZINC21525754 No No 2.753 -0.134 PubChem-16352732 No No 2.218 0.971 PubChem-136659176 No No 2.242 0.572 PubChem-135405892 No No 3.075 0.607 PubChem-126853168 No No 2.67 0.443 ZINC65196494 No No 3.354 -0.164 PubChem-41119771 No No 2.38 0.016 ZINC20113894 No No 2.574 0.429 ZINC20113894 No No 2.574 0.429 PubChem-71600230 No No 2.048 0.395 ZINC13365292 No No 2.604 0.134 LN00379431 No No 2.567 0.663 PubChem-3750748 No No 3.038 -0.137 ZINC4034881 No No 2.576 0.549 ZINC65196500 No No 3.156 -0.141 ZINC100771598 No No 3.008 0.471 PubChem-25352696 No No 2.552 1.059 PubChem-91428044 No No 1.439 0.563 ZINC35377731 No No 2.863 0.328 ZINC33057775 No No 2.503 0.769 As described in Table 3 , all selected compounds exhibited no Skin Sensitisation and no AMES toxicity. Additionally, the LD 50 values for oral rat toxicity, ranging from 1.439 to 3.354 mol/kg, suggest moderate acute toxicity levels, consistent with safety margins suitable for therapeutic use. Moreover, the maximum tolerated dose in humans’ ranges from -1.12 to 1.059 Log mg/kg/day. 4. Physicochemical properties and bioavailability Physicochemical properties were evaluated using Lipinski’s rule of five, 27 Ghose’s rule, 28 Veber’s rule, 29 Egan’s rule 30 and Muegge’s rule, 31 with results detailed in Table 4 . Based on these guidelines, it is suggested that for a compound to be effectively absorbed and administered orally, it must meet specific physicochemical parameters. These criteria serve as essential benchmarks for assessing the compound’s potential for bioavailability and oral absorption. Compounds with two or more violations are not considered in the further analysis (see Table S3 in supplementary data). Table 4. Physico-chemical properties based on the rules of Lipinski, Ghose, Veber, Egan and Muegge for the highest ranked compounds. Code Lipinski #violations Ghose #violations Veber #violations Egan #violations Muegge #violations ZINC17286185 0 1 0 0 0 ZINC35562518 0 1 0 0 0 ZINC64438506 0 1 0 0 0 PubChem-156469643 0 0 0 0 1 PubChem-89734004 0 0 0 0 0 PubChem-86747888 0 0 0 0 1 ZINC64438485 0 1 0 0 0 PubChem-121231416 0 0 0 0 0 ZINC21797190 0 0 0 0 0 PubChem-53794837 0 0 0 0 1 CHEMBL1652254 0 0 0 0 1 PUBChem 157251136 0 0 0 0 0 PubChem-42810820 0 0 0 0 0 ChemDiv-C260-2692 1 1 0 0 0 PubChem-135869534 0 0 0 0 0 PubChem-91487881 0 0 0 0 0 ZINC21525754 0 0 0 0 0 PubChem-16352732 0 0 0 0 0 PubChem-136659176 0 1 1 1 1 PubChem-135405892 0 0 0 0 0 PubChem-126853168 1 0 0 0 0 ZINC65196494 0 0 0 0 0 PubChem-41119771 1 1 0 0 1 ZINC20113894 0 0 0 0 0 PubChem-71600230 0 0 0 0 1 ZINC13365292 0 0 1 1 0 LN00379431 0 0 0 0 0 PubChem-3750748 1 1 0 0 1 ZINC4034881 0 0 0 0 0 ZINC65196500 0 0 0 0 0 ZINC100771598 0 0 0 0 0 PubChem-25352696 0 0 0 0 0 PubChem-91428044 0 0 0 0 0 ZINC33057775 0 0 0 0 0 5. Pharmacokinetic proprieties To predict the pharmacokinetic properties of absorption, distribution, metabolism and excretion (ADME), pkCSM web server was used to calculate the following parameters: water solubility (log mol/L), Caco-2 cell permeability, human intestinal absorption (HIA), blood-brain barrier (BBB) permeability, central nervous system (CNS) permeability and total clearance. Water solubility (LogS) indicates the solubility of a compound in water at 25°C. Generally, water-soluble drugs are more readily absorbed than lipid-soluble ones. in vitro Caco-2 cell permeability is a crucial measure of drug absorption, with a compound considered to have high Caco-2 permeability if its value surpasses 8 x 10 −6 cm/s. Within the pkCSM model, high Caco-2 permeability aligns with predicted values exceeding 0.90. The intestine typically serves as the primary site for drug absorption from orally administered solutions. A compound with absorbance below 30% is deemed poorly absorbed. An established gauge of blood-brain barrier (BBB) penetration is the log BB ratio, which reflects drug molecule concentrations in the brain and blood. Compounds with log BB > 0.3 exhibit high BBB permeability, while those with log BB < 1.0 show limited BBB distribution. Furthermore, central nervous system (CNS) permeability is a vital parameter for assessing the blood-brain permeability of a drug candidate, expressed as LogPS. Compounds with LogPS <-3 are considered incapable of penetrating the CNS. Based on these ADME properties, 18 compounds were selected for further analysis. The results for these compounds are detailed in Table 5 , while the other parameters are shown in Table S4 in the supplementary data. Table 5. Some ADME parameters of the top-ranked compounds. Code Water solubility Caco2 permeability Intestinal absorption (human) BBB permeability CNS permeability Total Clearance ZINC17286185 -5.117 0.818 83.062 -0.367 -2.784 0.727 ZINC35562518 -4.994 0.819 82.23 -0.389 -2.876 0.732 ZINC64438506 -5.467 1.165 96.008 -0.435 -2.363 0.804 ZINC64438485 -5.241 1.163 97.137 -0.98 -2.573 0.505 PubChem-121231416 -4.14 0.979 94.665 -0.031 -2.006 0.608 ZINC21797190 -4.273 1.083 96.539 -0.669 -2.597 0.623 PUBChem 157251136 -4.222 1.106 94.303 -0.893 -2.705 1.167 PubChem-42810820 -5.266 1.294 94.667 -0.394 -1.924 0.316 ChemDiv-C260-2692 -5.941 1.284 97.247 -0.422 -2.392 0.819 PubChem-91487881 -2.946 1.374 90.351 -0.966 -2.226 0.369 PubChem-126853168 -4.351 1.428 96.515 -0.399 -2.234 0.514 ZINC65196494 -4.278 1.131 98.641 -0.725 -2.5 0.947 PubChem-41119771 -4.601 1.251 95.633 0.709 -1.937 0.039 PubChem-3750748 -5.732 1.102 91.195 0.215 -1.931 -0.313 ZINC4034881 -4.991 1.18 97.628 -0.693 -2.477 0.972 PubChem-25352696 -2.773 1.444 98.767 -0.866 -2.82 0.534 PubChem-91428044 -5.217 1.3 94.768 0.399 -2.503 0.758 ZINC33057775 -4.528 1.202 93.212 -0.615 -2.241 -0.075 6. Docking Validation Docking validation was performed using AutoDock 4.2. The results are presented in Table 6 . Table 6. Binding energy (B.E), Intermolecular Energy (I.M.E), Internal Energy (I.E), Torsional Energy (T.E) and constant of inhibition (K.I). Code B.E (Kcal/mol) I.M.E (Kcal/mol) I.E (Kcal/mol) T.E (Kcal/mol) KI (nM) ChemDiv-C260-2692 -12.43 -14.82 -2.39 2.39 0.768 PubChem3750748 -12.3 -14.69 -2.05 2.39 0.958 PubChem25352696 -10.58 -12.63 -1.06 1.79 17.63 PubChem41119771 -11.23 -13.02 -1.22 1.79 5.91 PubChem42810820 -11.6 -12.79 -0.77 1.19 3.13 PubChem91428044 -10.2 -12.88 -1.1 2.68 33.36 PubChem91487881 -10.71 -13.09 -1.13 2.39 14.15 PubChem121231416 -11.63 -14.32 -0.86 2.68 2.98 PubChem126853168 -12.26 -13.75 -0.93 1.49 1.04 PUBChem157251136 -11.84 -14.23 -1.61 2.39 2.09 ZINC4034881 -11.51 -13.89 -2.1 2.39 3.67 ZINC17286185 -11.41 -14.39 -1.84 2.98 4.36 ZINC21797190 -12.37 -14.45 -1.22 2.09 0.862 ZINC33057775 -12.25 -14.04 -1.64 1.79 1.06 ZINC35562518 -12.22 -14.9 -1.74 2.68 1.11 ZINC64438485 -13.07 -15.76 -1.37 2.68 0.262 ZINC64438506 -13.11 -15.8 -1.37 2.68 0.244 ZINC65196494 -11.7 -13.49 -1.28 1.79 2.65 To identify the top CB1 agonist several criteria were taken into account including energy values (e.g. binding energy or Ki values), interactions with key amino acids in the CB1 binding site, number of hydrogen bonds and distances between hydrogen bonds. These interactions are essential for designing effective and selective CB1 agonists. 10 For instance, π-π interactions with aromatic residues (Phe296, Phe170, Phe174, Trp 279) and hydrogen bonds with Ser383 or Thr197 stabilize the agonist-receptor complex, while residues such as Phe379 and Asp366 play a key role in receptor activation through electrostatic interactions. 10 Based on these criteria three compounds were identified as CB1 receptor agonists: ZINC64438506, ZINC64438485 and PUBChem157251136. ZINC64438506: The docking analysis of CB1 receptor and ZINC64438506 selected agonist is shown in Table 7 and Figure 4 . The ZINC64438506 agonist was fixed in the CB1 binding pocket (cavity size = 2963 Å) through various type of interactions, including hydrogen bonds with key amino-acid residues SER A: 173 and SER A: 383, hydrophobic interactions with residues PHE A: 108, PHE A: 177, PHE A: 189, LEU A: 193, THR A: 197, PHE A: 268, TYR A: 275, LEU A: 276, TRP A: 279 and PHE A: 379 and π-π interaction with PHE A: 268. These interactions likely contribute to the compound’s low binding affinity (-13.11 Kcal/mol) and low inhibition constant (Ki = 0.244 nM) ( Table 6 ). The strong binding affinity may be attributed to the presence of short hydrogen bonds with SER A: 173 (3.34 Å) and SER A: 383 (51.79 Å). Table 7. Ligand-receptor interactions of the three highest-ranked agonists. Hydrophobic interactions Hydrogen Bonds π-Stacking Halogen Bonds Residue AA Distance Residue AA Distance H-A Distance D-A Residue AA Distance (Å) Residue AA Distance 174A PHE 3.83 173A SER 3.23 3.72 268A PHE 4.71 174A PHE 3.70 178A HIS 1.94 2.93 279A TRP 3.94 177A PHE 3.26 383A SER 2.04 2.81 193A LEU 3.37 197A THR 3.13 268A PHE 3.48 276A LEU 3.98 279A TRP 3.60 379A PHE 3.49 174A PHE 3.87 197A THR 1.94 2.93 174A PHE 5.09 177A PHE 3.68 383A SER 3.08 3.74 178A HIS 4.62 177A PHE 3.37 268A PHE 4.77 177A PHE 3.88 279A TRP 4.93 189A PHE 3.58 193A LEU 3.42 193A LEU 3.81 196A VAL 3.19 271A ILE 3.91 276A LEU 3.40 279A TRP 3.65 279A TRP 3.77 379A PHE 3.95 379A PHE 3.40 380A ALA 3.34 108A PHE 3.77 173A SER 3.34 3.82 268A PHE 4.77 177A PHE 3.82 383A SER 1.79 2.67 177A PHE 3.22 177A PHE 3.47 189A PHE 3.22 193A LEU 3.47 197A THR 3.59 268A PHE 3.63 275A TYR 3.13 276A LEU 3.26 279A TRP 3.40 279A TRP 3.71 279A TRP 3.25 279A TRP 3.56 379A PHE 3.76 Figure 4. Docking analysis of ZINC64438506 agonist with CB1 receptor. (a) 2D view of binding site interactions (b) 3D view of binding conformation. ZINC64438485: The docking analysis of CB1 receptor and ZINC64438485 selected agonist is shown in Table 7 and Figure 5 . The ZINC64438485 agonist was fixed in the CB1 binding pocket (cavity size = 2963 Å) through various type of interactions, including hydrogen bonds with key amino-acid residues THR A: 197 and SER A: 383, hydrophobic interactions with residues PHE A: 174, PHE A: 177, PHE A: 189, LEU A: 193, VAL A: 196 and ILE A: 271 and π-π interaction with PHE A: 174, HIS A: 178, PHE A: 268 and TRP A: 279. These interactions likely contribute to the compound’s low binding affinity (-13.07 Kcal/mol) and low inhibition constant (Ki = 0.262 nM) ( Table 6 ). The strong binding affinity may be attributed to the presence of short hydrogen bonds with THR A: 197 (1.94 Å) and SER A: 383 (3.08 Å). Figure 5. Docking analysis of ZINC64438485 agonist with CB1 receptor. (a) 2D view of binding site interactions (b) 3D view of binding conformation. PUBChem-157251136: The docking analysis of CB1 receptor and PUBChem-157251136 selected agonist is shown in Table 7 and Figure 6 . The PUBChem157251136 agonist was fixed in the CB1 binding pocket (cavity size = 2963 Å) through various type of interactions, including hydrogen bonds with key amino-acid residues SER A: 173, HIS A: 178 and SER A: 383, hydrophobic interactions with residues PHE A: 174, PHE A: 177, LEU A: 193, THR A: 197, PHE A: 268, LEU A: 276, TRP A: 279 and PHE A: 379 and π-π interaction with PHE A: 268 and TRP A: 279. These molecular interactions likely contribute to the compound’s low binding affinity (-11.84 Kcal/mol) and low inhibition constant (Ki = 2.09 nM) ( Table 6 ). The strong binding affinity may be attributed to the presence of three hydrogen bonds with SER A: 173 (3.23 Å), HIS A: 178 (1.94 Å) and SER A: 383 (2.04Å). Figure 6. Docking analysis of PUBChem_157251136 agonist with CB1 receptor. (a) 2D view of binding site interactions (b) 3D view of binding conformation. 7. MD simulations While molecular docking analyses are enough to understand possible bindings based on ligand positioning and receptor-ligand interactions, it should be noted that such methodologies evaluate the flexibility of the ligand only while keeping the protein in a rigid form. Thus, in order to evaluate the best-docked candidates for binding pose stability and dynamics of protein conformation, molecular dynamics (MD) simulations were performed over 100 ns. The results of the MD simulations, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and Ligand-Receptor Interaction Plot analyses, can be found in Figures 7 – 10 . These studies provide insights into the dynamic behavior and stability of the protein–ligand complexes over time. Figure 7. Plots of RMSD over the 100 ns MD simulation. The black color was the CB1_ZINC64438506 complex, the red was the CB1_ZINC64438485 complex and the green was the CB1_PUBChem157251136 complex. 7.1 Root Mean Square Deviation (RMSD) RMSD analysis was carried out for the protein backbone to have an idea of each protein-ligand complex’s structural stability during the simulation. 32 , 33 The results are shown in Figure 7 . As shown in Figure 7 , mean RMSD values for the CB1_ZINC64438506, CB1_ZINC64438485 and CB1_PUBChem157251136 complex are 0.29 nm, 0.45 nm and 0.64 nm, respectively. The global RMSD values for CB1_ZINC64438506, CB1_ZINC64438485 are small which indicates that these ligands remain stable during the simulation of 100 ns and remained in the binding pocket of the CB1 receptor. The stability of the ligands can be attributed to several strong hydrogen bonds being mediated between these agonists and some of the key amino acids positioned within the binding pocket of the CB1 receptor. 7.2 Root mean square fluctuation (RMSF) Root mean square fluctuation (RMSF) was used to determine the rigid and flexible regions of the CB1 receptor over the 100 ns of MD simulations. 14 RMSF has been term definition, instead of just RMSD values, so that the maximum range of motion of a bound ligand can be seen. RMSF is defined as a standard measure of deviation of a molecule from its initial position. 34 Molecules and residues should not present a high value, which indicates a flexibility, and those that appears low value has a greater rigidity. The RMSF plot for all complexes ( Figure 8 ) indicates that most residues located in the CB1 receptor has a low RMSF value, indicating they were rigid and retained stability over the entire 100 ns MD simulation. Figure 8. Plots of RMSF over the 100 ns MD simulation. The black color was the CB1_ZINC64438506 complex, the red was the CB1_ZINC64438485 complex and the green was the CB1_PUBChem157251136 complex. 7.3 Radius of gyration (Rg) The radius of gyration (Rg) is another important metric measured during MD simulations to determine spatial characteristics of a protein-ligand complex. The radius of gyration is the root mean square distance of all atoms making up a given structure from a relative center common point (the center of mass) and signifies extension versus folding of the given structure. Therefore, a lower Rg would imply stability and a more tightly folded structure, while a higher Rg would imply extension and flexibility of possible structures. In this work, the Rg profiles remained stable over the course of 100 ns of MD simulation for the complexes CB1_ZINC64438506 (mean = 4.70 nm; Figure 9 ) and CB1_ZINC64438485 (mean = 4.72 nm), while the complex CB1_PUBChem157251136 decreased in Rg (mean = 2.81 nm). Figure 9. Plots of Rg during the 100 ns of MD simulation. The black color represents the CB1_ZINC64438506 complex, the red color represents the CB1_ZINC64438485 complex and the green color represents the CB1_PUBChem157251136 complex. 7.4 Hydrogen bonds One of the main factors influencing the affinity of a molecule for the protein binding pocket is its ability to form and maintain hydrogen bonds with the binding site residues. The stability of the selected agonist was assessed by analyzing the hydrogen bonds between the ligand and the protein. The results are presented in Figure 10 . Figure 10. Plot of H-bonds during the 100 ns of MD simulation. The black color represents the CB1_ZINC64438506 complex, the red color represents the CB1_ZINC64438485 complex and the green color represents the CB1_PUBChem157251136 complex. Compounds ZINC64438506, ZINC64438485, and PUBChem157251136 form two, two and three hydrogen bonds with the CB1 binding site, respectively, indicating strong and specific interactions with the protein. These results are consistent with the docking results. 8. MM-GBSA calculation for the top three ranked compounds The binding free energy of all complexes was calculated to revalidate the binding affinity obtained from molecular docking analysis. The results are presented in Table 8 . A ΔG bind value below -7 Kcal/mol indicates strong binding, a value between -5 and -7 kcal/mol suggests moderate binding, and a value between -2 and -5 kcal/mol corresponds to weak binding. 35 The results show that all the agonists exhibit exceptionally low binding free energies, indicating a remarkable affinity for the target protein. Table 8. MM-GBSA calculations for the top three ranked compounds. Complex ΔG GAS (Kcal/mol) ΔG SOLV (Kcal/mol) ΔG bind (Kcal/mol) CB1_ZINC64438506 -80.76 30.99 -49.77 CB1_PUBChem157251136 -107.10 76.51 -30.59 CB1_ZINC64438485 -82.29 32.31 -49.98 The results also highlight the crucial role of Van der Waals and electrostatic interactions in mediating protein-ligand binding throughout the molecular dynamics simulations ( Figure 11 ). Figure 11. Binding free energy plot of the three highest ranked agonists. 9. Quinazoline-2,4(1H,3H)-dione derivatives Quinazoline-2,4(1H,3H)-dione derivatives represent a highly promising class of heterocyclic compounds with broad therapeutic potential, particularly as anticancer, 36 – 38 antibacterial, 39 , 40 antihypertensive, 41 phosphodiesterase (PDE) 4 inhibition, 42 5-HT3A receptor antagonist, 43 anti-inflammatory, 44 and anan up-and-comingtiviral agents. 45 Their fused benzopyrimidinedione scaffold provides an excellent pharmacophore for targeting key biological pathways ( Figure 12 ). According to the article’s findings, quinazoline-2,4(1H,3H)-dione derivatives may be the first of a new class of CB1 agonists. Figure 12. Quinazoline-1, 4(1H, 3H)-dione scaffold and the three highest-ranked agonists (ZINC64438506, PUBChem157251136 and ZINC64438485). 10. Different synthesis routes of substituted quinazoline-2,4-dione scaffold To synthesise the substituted quinazoline-2,4-dione and the highest-ranking agonists identified by virtual screening, namely ZINC64438506, PUBChem157251136 and ZINC64438485, several plausible synthesis strategies were developed based on established methodologies. 43 These routes use various precursors such as 2-aminobenzoic acid, 44 methyl 2-nitrobenzoate, 2-iodobenzamides 46 or 2H-benzo [d][1,3]oxazine-2,4(1H)-dione 47 ( Figure 13 ). The choice of starting material is guided by both its availability and synthetic feasibility. Depending on the substrate, the desired compounds can be obtained by catalytic transformations, particularly transition metal-catalysed couplings, or by intramolecular cyclisation reactions that form the characteristic quinazoline skeleton. The synthetic flexibility offered by these precursors allows the reaction conditions to be adjusted to optimise yield and purity, making them suitable candidates for further pharmacological evaluation and development. Figure 13. Different synthesis routes of substituted quinazoline-2, 4-dione. To synthesise substituted quinazoline-2,4-dione and the top-ranked agonists identified by virtual screening - namely ZINC64438506, PUBChem157251136 and ZINC64438485 - several plausible synthetic strategies have been devised, based on established methodologies. 46 These routes use various precursors such as 2-aminobenzoic acid, 47 methyl 2-nitrobenzoate 48 which undergoes catalytic hydrogenation under mild and green conditions (1 atm of H 2 in ethanol at room temperature), 49 , 50 2-iodobenzamides 51 or 2H-benzo [d][1,3]oxazine-2,4(1H)-dione 52 ( Figure 13 ). The choice of starting material is guided by both availability and synthetic feasibility. Depending on the substrate, the desired compounds can be accessed by catalytic transformations, including transition metal-catalysed couplings, or by intramolecular cyclisation reactions that form the characteristic quinazoline scaffold. The synthetic flexibility offered by these precursors allows reaction conditions to be fine-tuned to optimize yield and purity, making them suitable candidates for further pharmacological evaluation and development. IV. Conclusion In this study, pharmacophore-based virtual screening was conducted to identify the best CB1 agonist based on the pharmacophoric features of AM11542. The optimal pharmacophore model comprised two hydrogen bond acceptors (HBA), one aromatic ring (AR), and two hydrophobic centers (HY). This model was subsequently applied to screen a database of more than five million compounds, including Molprot with 4,742,020 molecules, ChEMBL34 with 2,264,112 molecules, ZINC with 13,127,550 molecules, ChemDiv with 1,456,120 molecules, ChemSpace with 50,181,678 molecules, Enamine with 4,117,328 molecules, MCULE with 39,843,637 molecule, MCULE-ULTIMATE with 126,471,502 molecules, NCI Open Chemical Repository with 52,237 molecules, LabNetwork with 1,794,286 molecules and PubChem with 103,302,052 molecules. Based on the generated pharmacophore model, 433 compounds were selected for further evaluation. Subsequent molecular docking refined this selection to 61 high-affinity ligands (≤-9.00 kcal/mol), which were refined to 18 promising leads through ADME-Tox analysis. Among these, three compounds were selected as potential agonists of the CB1 receptor based on their binding affinity and strong interactions with key binding pocket residues (Ser383, Ser173, His178, and Thr197). Molecular dynamics simulations using Gromacs 2024.4 confirmed the structural stability of these complexes, with low RMSD (<1 nm) and RMSF (<1 nm) values, indicating minimal conformational fluctuations. MM-GBSA calculations further validated the thermodynamic stability, with binding free energies ranging from -30.59 to -49.98 kcal/mol, reinforcing their potential as potent CB1 agonists. The three selected compounds shared a common quinazolinz-2,4(1H,3H)-dione scarfed, indicating that derivatives of this structure could pave the way for developing new CB1 receptor agonists. Data availability statement Underlying data No underlying data are associated with this article. Extended data Repository name: “Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A ppharmacophore-based virtual screening workflow and Lead discovery” https://doi.org/ 10.5281/zenodo.17274156 . 53 This project contains the following extended data: • Supplementary Table 1 . 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Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 27 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Fes-Boulemane, Morocco 2 Universite Ibn Zohr Faculte des Sciences Agadir, Agadir, Souss-Massa-Draa, Morocco 3 Universite Sidi Mohamed Ben Abdellah Faculte des Sciences Dhar El Mahraz-Fes, Fes, Fes-Boulemane, Morocco Abdellah EL AISSOUQ Roles: Methodology, Writing – Original Draft Preparation, Writing – Review & Editing MOURAD STITOU Roles: Investigation, Methodology, Validation, Visualization Mohamed Enneiymy Roles: Methodology, Supervision, Validation, Visualization Said El Rhabori Roles: Methodology, Resources, Software, Validation Hicham Zaitan Roles: Data Curation, Supervision, Validation, Visualization Abdelkrim Ouammou Roles: Data Curation, Investigation, Methodology, Validation Fouad Khalil Roles: Methodology, Supervision, Validation, Visualization Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 11 Mar 2026, 14:1322 https://doi.org/10.12688/f1000research.171433.3 version 2 Revised Published: 06 Jan 2026, 14:1322 https://doi.org/10.12688/f1000research.171433.2 version 1 Published: 27 Nov 2025, 14:1322 https://doi.org/10.12688/f1000research.171433.1 Copyright © 2026 EL AISSOUQ A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article EL AISSOUQ A, STITOU M, Enneiymy M et al. Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.12688/f1000research.171433.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 06 Jan 2026 Revised Views 0 Cite How to cite this report: Chtita S and Rossafi B. Reviewer Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.194643.r449401 ) The direct URL for this report is: https://f1000research.com/articles/14-1322/v2#referee-response-449401 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Feb 2026 Samir Chtita , Hassan II University of Casablanca, Morocco, Morocco Bouchra Rossafi , Institution: Hassan II University of Casablanca, Morocco, Morocco, Morocco Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.194643.r449401 The manuscript titled " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery ", present an in silico investigation aimed at identifying new CB1 agonists derived from quinazoline-2,4(1H,3H)-dione scaffolds, employing a pharmacophore-based virtual screening strategy ... Continue reading READ ALL The manuscript titled " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery ", present an in silico investigation aimed at identifying new CB1 agonists derived from quinazoline-2,4(1H,3H)-dione scaffolds, employing a pharmacophore-based virtual screening strategy followed by molecular docking, MD simulations and MMGBSA calculations. While the study provides interesting results, some aspects require improvement, including: The authors should more clearly demonstrate the novelty and originality of the present study in the Introduction section, particularly in comparison with previously reported CB1 agonists and related computational studies. After the pharmacophore-based virtual screening, the authors proceeded with molecular docking. However, the rationale behind the selection of 61 compounds with a docking score threshold of −9 kcal/mol as top molecules is not clearly justified. The authors should explain why this specific cutoff was chosen. Although a reference agonist (AM11542) was used for the generation of the pharmacophore model, it is not clear whether this compound was subsequently used as a reference drug in the docking, ADMET and MD analyses. In the context of the targeted pathology and the central role of the CB1 receptor, the analysis of BBB permeability and central nervous system CNS parameters is highly relevant. These aspects should be more thoroughly analyzed and discussed. The Introduction identifies several key residues involved in CB1 receptor activation. However, it is not clearly demonstrated whether the identified compounds effectively interact with these key residues. Comparing MM-GBSA results with docking scores or reference compounds could strengthen the interpretation. The quality and clarity of the docking figures should be improved. In the table 2, the unit for binding energy should be written as “kcal” instead of “Kcal” to follow standard scientific notation. The manuscript would benefit from a dedicated section discussing the limitations of the study. Experimental validation of the identified compounds perspectives should be included in the Conclusion. Information included on the different modeling techniques in material and methods section is quite trivial and should be more detailed. To enhance the introduction of the computational section, it is recommended to provide a detailed discussion and comparison of different methods as reported in the literature. In order to facilitate a better understanding for readers, the following previous studies employing docking and molecular dynamic in this research area can be cited: Refer to reference 1, 2, 3, & 4 Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Daoui O, Elkhattabi S, Chtita S: Rational design of novel pyridine-based drugs candidates for lymphoma therapy. Journal of Molecular Structure . 2022; 1270 . Publisher Full Text 2. Chalkha M, Chebbac K, Nour H, Nakkabi A, et al.: In vitro and in silico evaluation of the antimicrobial and antioxidant activities of spiropyrazoline oxindole congeners. Arabian Journal of Chemistry . 2024; 17 (1). Publisher Full Text 3. Khedraoui M, Abchir O, Nour H, Yamari I, et al.: An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE. Pharmaceuticals . 2024; 17 (7). Publisher Full Text 4. Daoui O, Elkhattabi S, Chtita S: Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach. Structural Chemistry . 2022; 33 (5): 1667-1690 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Molecular modeling We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Chtita S and Rossafi B. Reviewer Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.194643.r449401 ) The direct URL for this report is: https://f1000research.com/articles/14-1322/v2#referee-response-449401 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 11 Mar 2026 ABDELLAH EL AISSOUQ , Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco 11 Mar 2026 Author Response reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response ... Continue reading reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response to Q3: the agonist (AM11542) was selected as the reference coumpound for the whole study response to Q4-9: your remarks have been taken into account in the revised manuscript response to Q10: all selected compounds are known drugs reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response to Q3: the agonist (AM11542) was selected as the reference coumpound for the whole study response to Q4-9: your remarks have been taken into account in the revised manuscript response to Q10: all selected compounds are known drugs Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 11 Mar 2026 ABDELLAH EL AISSOUQ , Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco 11 Mar 2026 Author Response reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response ... Continue reading reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response to Q3: the agonist (AM11542) was selected as the reference coumpound for the whole study response to Q4-9: your remarks have been taken into account in the revised manuscript response to Q10: all selected compounds are known drugs reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response to Q3: the agonist (AM11542) was selected as the reference coumpound for the whole study response to Q4-9: your remarks have been taken into account in the revised manuscript response to Q10: all selected compounds are known drugs Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 27 Nov 2025 Views 0 Cite How to cite this report: Fawzi M. Reviewer Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.189041.r439371 ) The direct URL for this report is: https://f1000research.com/articles/14-1322/v1#referee-response-439371 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Dec 2025 Mourad Fawzi , Faculty of Sciences Semlalia, Cadi Ayyad University, Department of Chemistry, Laboratory of Molecular Chemistry, Marrakech, Morocco Approved VIEWS 0 https://doi.org/10.5256/f1000research.189041.r439371 The study entitled " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is ... Continue reading READ ALL The study entitled " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3 . Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6 : The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Organic Chemistry, Medicinal Chemistry I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Fawzi M. Reviewer Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.189041.r439371 ) The direct URL for this report is: https://f1000research.com/articles/14-1322/v1#referee-response-439371 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Jan 2026 ABDELLAH EL AISSOUQ , Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco 14 Jan 2026 Author Response Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large ... Continue reading Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3. Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6: The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: Response to Questions 1-6: We thank the reviewer for their insightful comments. All remarks have been addressed in the revised manuscript." Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3. Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6: The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: Response to Questions 1-6: We thank the reviewer for their insightful comments. All remarks have been addressed in the revised manuscript." Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Jan 2026 ABDELLAH EL AISSOUQ , Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco 14 Jan 2026 Author Response Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large ... Continue reading Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3. Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6: The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: Response to Questions 1-6: We thank the reviewer for their insightful comments. All remarks have been addressed in the revised manuscript." Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3. Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6: The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: Response to Questions 1-6: We thank the reviewer for their insightful comments. All remarks have been addressed in the revised manuscript." Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: En-nahli F. Reviewer Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.189041.r437306 ) The direct URL for this report is: https://f1000research.com/articles/14-1322/v1#referee-response-437306 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Dec 2025 Fatima En-nahli , University of Moulay Ismail, Meknes, Morocco Approved VIEWS 0 https://doi.org/10.5256/f1000research.189041.r437306 Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant ... Continue reading READ ALL Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoDock4). It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Overall, the manuscript represents an interesting contribution, and after addressing these minor points, it will be suitable for publication. Thank you again for entrusting me with the review of this manuscript. Kind regards, Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: chimie informatique I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT En-nahli F. Reviewer Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.189041.r437306 ) The direct URL for this report is: https://f1000research.com/articles/14-1322/v1#referee-response-437306 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Jan 2026 ABDELLAH EL AISSOUQ , Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco 14 Jan 2026 Author Response Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual ... Continue reading Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: 1. The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. Response to Q1: the title has been changed to “Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery” 2. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. Response to Q2: The molecular dynamics (MD) simulations are mentioned in the abstract, as shown in the following excerpt from the abstract section “Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. 3. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoD ock4).It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. Response to Q3: We thank the reviewer for raising this important point regarding our docking strategy. The double-docking approach was implemented to enhance the robustness and reliability of our virtual screening workflow. 4. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. 5. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. 6. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Response to Q4, 4 and 6 : The identified compounds are promising drug candidates Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: 1. The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. Response to Q1: the title has been changed to “Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery” 2. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. Response to Q2: The molecular dynamics (MD) simulations are mentioned in the abstract, as shown in the following excerpt from the abstract section “Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. 3. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoD ock4).It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. Response to Q3: We thank the reviewer for raising this important point regarding our docking strategy. The double-docking approach was implemented to enhance the robustness and reliability of our virtual screening workflow. 4. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. 5. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. 6. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Response to Q4, 4 and 6 : The identified compounds are promising drug candidates Competing Interests: Not applicable. The authors declare no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Jan 2026 ABDELLAH EL AISSOUQ , Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco 14 Jan 2026 Author Response Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual ... Continue reading Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: 1. The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. Response to Q1: the title has been changed to “Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery” 2. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. Response to Q2: The molecular dynamics (MD) simulations are mentioned in the abstract, as shown in the following excerpt from the abstract section “Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. 3. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoD ock4).It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. Response to Q3: We thank the reviewer for raising this important point regarding our docking strategy. The double-docking approach was implemented to enhance the robustness and reliability of our virtual screening workflow. 4. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. 5. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. 6. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Response to Q4, 4 and 6 : The identified compounds are promising drug candidates Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: 1. The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. Response to Q1: the title has been changed to “Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery” 2. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. Response to Q2: The molecular dynamics (MD) simulations are mentioned in the abstract, as shown in the following excerpt from the abstract section “Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. 3. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoD ock4).It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. Response to Q3: We thank the reviewer for raising this important point regarding our docking strategy. The double-docking approach was implemented to enhance the robustness and reliability of our virtual screening workflow. 4. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. 5. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. 6. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Response to Q4, 4 and 6 : The identified compounds are promising drug candidates Competing Interests: Not applicable. The authors declare no competing interests. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 27 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 11 Mar 26 read Version 2 (revision) 06 Jan 26 read Version 1 27 Nov 25 read read Fatima En-nahli , University of Moulay Ismail, Meknes, Morocco Mourad Fawzi , Laboratory of Molecular Chemistry, Marrakech, Morocco Bouchra Rossafi , Institution: Hassan II University of Casablanca, Morocco, Morocco, Morocco Samir Chtita , Hassan II University of Casablanca, Morocco, Morocco Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Chtita S et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Mar 2026 | for Version 3 Bouchra Rossafi , Institution: Hassan II University of Casablanca, Morocco, Morocco, Morocco Samir Chtita , Hassan II University of Casablanca, Morocco, Morocco 0 Views copyright © 2026 Chtita S et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have addressed all of my recommendations, and I recommend the indexing of the article Competing Interests No competing interests were disclosed. Reviewer Expertise Molecular modeling We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Rossafi B and Chtita S. Peer Review Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.197019.r466648) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1322/v3#referee-response-466648 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Chtita S et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Feb 2026 | for Version 2 Samir Chtita , Hassan II University of Casablanca, Morocco, Morocco Bouchra Rossafi , Institution: Hassan II University of Casablanca, Morocco, Morocco, Morocco 0 Views copyright © 2026 Chtita S et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript titled " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery ", present an in silico investigation aimed at identifying new CB1 agonists derived from quinazoline-2,4(1H,3H)-dione scaffolds, employing a pharmacophore-based virtual screening strategy followed by molecular docking, MD simulations and MMGBSA calculations. While the study provides interesting results, some aspects require improvement, including: The authors should more clearly demonstrate the novelty and originality of the present study in the Introduction section, particularly in comparison with previously reported CB1 agonists and related computational studies. After the pharmacophore-based virtual screening, the authors proceeded with molecular docking. However, the rationale behind the selection of 61 compounds with a docking score threshold of −9 kcal/mol as top molecules is not clearly justified. The authors should explain why this specific cutoff was chosen. Although a reference agonist (AM11542) was used for the generation of the pharmacophore model, it is not clear whether this compound was subsequently used as a reference drug in the docking, ADMET and MD analyses. In the context of the targeted pathology and the central role of the CB1 receptor, the analysis of BBB permeability and central nervous system CNS parameters is highly relevant. These aspects should be more thoroughly analyzed and discussed. The Introduction identifies several key residues involved in CB1 receptor activation. However, it is not clearly demonstrated whether the identified compounds effectively interact with these key residues. Comparing MM-GBSA results with docking scores or reference compounds could strengthen the interpretation. The quality and clarity of the docking figures should be improved. In the table 2, the unit for binding energy should be written as “kcal” instead of “Kcal” to follow standard scientific notation. The manuscript would benefit from a dedicated section discussing the limitations of the study. Experimental validation of the identified compounds perspectives should be included in the Conclusion. Information included on the different modeling techniques in material and methods section is quite trivial and should be more detailed. To enhance the introduction of the computational section, it is recommended to provide a detailed discussion and comparison of different methods as reported in the literature. In order to facilitate a better understanding for readers, the following previous studies employing docking and molecular dynamic in this research area can be cited: Refer to reference 1, 2, 3, & 4 Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Daoui O, Elkhattabi S, Chtita S: Rational design of novel pyridine-based drugs candidates for lymphoma therapy. Journal of Molecular Structure . 2022; 1270 . Publisher Full Text 2. Chalkha M, Chebbac K, Nour H, Nakkabi A, et al.: In vitro and in silico evaluation of the antimicrobial and antioxidant activities of spiropyrazoline oxindole congeners. Arabian Journal of Chemistry . 2024; 17 (1). Publisher Full Text 3. Khedraoui M, Abchir O, Nour H, Yamari I, et al.: An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE. Pharmaceuticals . 2024; 17 (7). Publisher Full Text 4. Daoui O, Elkhattabi S, Chtita S: Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach. Structural Chemistry . 2022; 33 (5): 1667-1690 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Molecular modeling We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 11 Mar 2026 ABDELLAH EL AISSOUQ, Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco reponse to Q1: your remarks have been taken into account in the revised manuscript response to Q2: this threshold was chosen based on the reference molecule (see sup data) response to Q3: the agonist (AM11542) was selected as the reference coumpound for the whole study response to Q4-9: your remarks have been taken into account in the revised manuscript response to Q10: all selected compounds are known drugs View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Chtita S and Rossafi B. Peer Review Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.194643.r449401) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1322/v2#referee-response-449401 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Fawzi M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Dec 2025 | for Version 1 Mourad Fawzi , Faculty of Sciences Semlalia, Cadi Ayyad University, Department of Chemistry, Laboratory of Molecular Chemistry, Marrakech, Morocco 0 Views copyright © 2026 Fawzi M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study entitled " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3 . Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6 : The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: " Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery " Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Bioinformatics, Organic Chemistry, Medicinal Chemistry I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 14 Jan 2026 ABDELLAH EL AISSOUQ, Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco Reviewer #2: The study entitled "Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery" presents a pharmacophore-based virtual screening of a large database aimed at developing new CB1 agonists. I believe this work is suitable for publication in F1000 Research before the minor revisions below: Comments for the authors: Page 17: "form two, two and three hydrogen bonds with the CB1 binding site, respectively, …" – The word "two" is repeated; please remove one. Some abbreviations are not clearly defined in the manuscript, such as GPCR and ICL3. Please ensure all abbreviations are spelled out upon first use. Page 19: The sentence ̋Translated with DeepL.com (free version)̋ should be removed. More details about the advantage of work in conclusion are required Grammatical errors and typos are present throughout the manuscript. A thorough proofreading is recommended to improve clarity and language quality. Regarding Table 6: The reported Kᵢ values indicate that the screened compounds exhibit promising drug-like properties. Therefore, I suggest modifying the title to better reflect this finding. Proposed revision: Response to Questions 1-6: We thank the reviewer for their insightful comments. All remarks have been addressed in the revised manuscript." View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Fawzi M. Peer Review Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.189041.r439371) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1322/v1#referee-response-439371 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 En-nahli F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Dec 2025 | for Version 1 Fatima En-nahli , University of Moulay Ismail, Meknes, Morocco 0 Views copyright © 2026 En-nahli F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoDock4). It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Overall, the manuscript represents an interesting contribution, and after addressing these minor points, it will be suitable for publication. Thank you again for entrusting me with the review of this manuscript. Kind regards, Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise chimie informatique I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 14 Jan 2026 ABDELLAH EL AISSOUQ, Universite Sidi Mohamed Ben Abdellah Faculte des Sciences et Techniques de Fes, Fes, Morocco Reviewer #1: Dear Editor, I would like to thank you for the opportunity to review the manuscript entitled “Quinazoline-2,4(1H,3H)-dione derivatives as a new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and Lead discovery.” The study addresses a relevant topic and provides valuable insights into CB1 agonist identification through an in silico workflow. Overall, the manuscript is promising; however, I believe that minor revisions are required to improve clarity and scientific rigor. Please find below my main remarks for the authors: 1. The current title would benefit from being reformulated to make it more concise and more attractive to readers. A clearer title highlighting the novelty and the integrated workflow is recommended. Response to Q1: the title has been changed to “Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery” 2. The authors mention the use of molecular dynamics (MD) simulations in the Materials and Methods section, but MD is not mentioned in the Abstract. For coherence and completeness, this important component of the workflow should be briefly stated in the Abstract. Response to Q2: The molecular dynamics (MD) simulations are mentioned in the abstract, as shown in the following excerpt from the abstract section “Molecular dynamics simulations (100 ns, GROMACS) demonstrated structural stability (RMSD < 1 nm) and low conformational flexibility (RMSF < 1 nm) for all complexes. MM-GBSA binding free energy calculations further confirmed the thermodynamic stability of all complexes, with interaction energies ranging from -30.59 to -49.98 kcal/mol. 3. In the graphical abstract, the authors indicate two docking steps (first using AutoDock Vina, then AutoD ock4).It would be helpful to briefly clarify the rationale behind this double-docking strategy and how each tool contributes to the screening workflow. Response to Q3: We thank the reviewer for raising this important point regarding our docking strategy. The double-docking approach was implemented to enhance the robustness and reliability of our virtual screening workflow. 4. The authors are encouraged to integrate a comparative discussion between the pharmacophore hits, docking scores, and the stability of key amino acids involved in ligand recognition, supported by MD fluctuation analysis (e.g., RMSF). This would reinforce the consistency and predictive power of the multi-step in silico approach. 5. Since none of the proposed candidates were synthesized or experimentally validated, the authors should clearly state the value of the in silico-only approach and discuss its limitations. It is also recommended to suggest potential collaboration with experimental researchers for future synthesis and biological evaluation. 6. Given that the identified compounds appear to be promising drug candidates, it would be appropriate to add a short section in the conclusion outlining perspectives for future work. Response to Q4, 4 and 6 : The identified compounds are promising drug candidates View more View less Competing Interests Not applicable. The authors declare no competing interests. reply Respond Report a concern En-nahli F. Peer Review Report For: Quinazoline-2,4(1H,3H)-dione derivatives as new class of CB1 Agonists: A pharmacophore-based virtual screening workflow and drug discovery [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2026, 14 :1322 ( https://doi.org/10.5256/f1000research.189041.r437306) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1322/v1#referee-response-437306 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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