<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.145391.2</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3&#x03b2; for Alzheimer&#x2019;s disease</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 2; peer review: 2 approved, 1 approved with reservations, 1 not approved]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Shri</surname>
                        <given-names>Suggala Ramya</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Nayak</surname>
                        <given-names>Yogendra</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-0508-1394</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Ranganath Pai</surname>
                        <given-names>Sreedhara</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-2017-9533</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:ksr.pai@manipal.edu">ksr.pai@manipal.edu</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>27</day>
                <month>8</month>
                <year>2024</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2024</year>
            </pub-date>
            <volume>13</volume>
            <elocation-id>773</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>22</day>
                    <month>8</month>
                    <year>2024</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Shri SR et al.</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/13-773/pdf"/>
            <abstract>
                <sec>
                    <title>Background</title>
                    <p>The discovery of an ideal and effective therapy is urgently required for the treatment of Alzheimer&#x2019;s disease. The main pathological hallmarks of Alzheimer&#x2019;s disease that appear before the clinical symptoms are neurofibrillary tangles, amyloid plaques, brain inflammation, and neuronal atrophy throughout the cerebral cortex and hippocampus. GSK-3&#x03b2; (Glycogen Synthase Kinase-3&#x03b2;) is regarded as the most important and promising target for therapeutic use because GSK-3&#x03b2; expression levels increase with age and are the most abundant and hyperactive in the brains of patients with Alzheimer&#x2019;s disease.</p>
                </sec>
                <sec>
                    <title>Methods</title>
                    <p>We used Maestro, which is Schrodinger, for our computational simulation studies. In the present work, we have used different modules that were used in previous studies with a little modification, the modules such as Protein Preparation with the help of Protein Preparation Wizard, Ligand Preparation with the help of LigPrep, for ADME (Absorption, Distribution, Metabolism and Excretion) prediction we used Qikprop, Docking studies we used Glide module, Binding energy prediction we used Prime and Molecular dynamic simulation studies by Desmond</p>
                </sec>
                <sec>
                    <title>Results</title>
                    <p>Our focus is mainly on an 
                        <italic toggle="yes">in-silico</italic> approach, focusing on library generation; we first drew an imidazo [1,5-a]pyridine-3-carboxamide (IMID 2) scaffold structure at Enamine and subjected it to a substructure search to target the receptor grid region (ATP-competitive site) of 6Y9R. They were then subjected to various screening processes. Finally, we selected nine compounds and subjected them to molecular dynamic simulation studies.</p>
                </sec>
                <sec>
                    <title>Conclusions</title>
                    <p>Nine compounds showed good results with the most stable interactions. Further experiments and studies are required to confirm these results.</p>
                </sec>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Alzheimer&#x2019;s disease</kwd>
                <kwd>GSK-3&#x03b2;</kwd>
                <kwd>ATP-competitive study</kwd>
                <kwd>Protein preparation</kwd>
                <kwd>Ligand Preparation</kwd>
                <kwd>Qikprop</kwd>
                <kwd>Glide module</kwd>
                <kwd>Prime MM-GBSA</kwd>
                <kwd>Molecular dynamic simulation.</kwd>
            </kwd-group>
            <funding-group>
                <award-group id="fund-1" xlink:href="http://dx.doi.org/10.13039/501100001411">
                    <funding-source>Indian Council of Medical Research</funding-source>
                    <award-id>2021-11506_F1</award-id>
                </award-group>
                <funding-statement>Indian Council of Medical Research (ICMR), New Delhi, India, for ICMR-SRF (#2021-11506_F1) to Suggala Ramya Shri.</funding-statement>
                <funding-statement>
                    <italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic>
                </funding-statement>
            </funding-group>
        </article-meta>
        <notes>
            <sec sec-type="version-changes">
                <label>Revised</label>
                <title>Amendments from Version 1</title>
                <p>In the revised manuscript, we have updated the abstract by incorporating details about the various modules utilised in the study, and we have added a new paragraph in the introduction discussing the selection of the core imidazole scaffold.</p>
            </sec>
        </notes>
    </front>
    <body>
        <sec id="sec5" sec-type="intro">
            <title>Introduction</title>
            <p>One of the greatest threats to public health is neurodegenerative diseases because there is no exact therapy. Therefore, the discovery of an ideal and effective therapy is urgently needed for the treatment of Alzheimer&#x2019;s disease.
                <sup>
					
                    <xref ref-type="bibr" rid="ref1">1</xref>
				</sup> Only five to seven cases are due to genetic mutations, and the remaining cases are due to environmental factors and sporadic mutations. The main pathological hallmarks of Alzheimer&#x2019;s disease that appear before the clinical symptoms are neurofibrillary tangles, amyloid plaques, brain inflammation, and neuronal atrophy throughout the cerebral cortex and hippocampus.
                <sup>
					
                    <xref ref-type="bibr" rid="ref2">2</xref>
				</sup> Memory loss can be elicited in Alzheimer&#x2019;s patients, such as episodic short-term memory impairment followed by a lack of motivation, disorganization, and impairment in solving problems, judgment, and executive functioning. In early stage impairments, such as visuospatial skills, neuropsychiatric symptoms are most common in mild and late-stage Alzheimer&#x2019;s disease.
                <sup>
					
                    <xref ref-type="bibr" rid="ref3">3</xref>
				</sup> The heritability of AD is estimated to be between 60% and 80%, and these components allow for the identification and determination of pathophysiological processes, diagnostic markers, biological targets, and new treatment targets through genomics translational studies.
                <sup>
					
                    <xref ref-type="bibr" rid="ref4">4</xref>
				</sup> Glycogen synthase kinase-3 (GSK-3) is a member of the protein kinase family and is widely expressed in tissues. GSK-3 is a Serine/Threonine kinase that transfers a phosphate group to either the serine or threonine residue of its substrate target.
                <sup>
					
                    <xref ref-type="bibr" rid="ref5">5</xref>
				</sup> GSK-3&#x03b2; is regarded as the most important and promising therapeutic target. The etiology and pathogenesis of AD are not completely understood. However, available treatments have failed to show novel approaches and effectiveness, and the efficacy of drugs varies from one person to another.
                <sup>
					
                    <xref ref-type="bibr" rid="ref6">6</xref>
				</sup> GSK-3&#x03b2; expression levels increase with age and are most abundant and hyperactive in the brains of patients with AD. Hence, dysregulation of GSK-3&#x03b2; automatically affects amyloid beta plaques, which have been previously shown in in vitro and in vivo Alzheimer&#x2019;s disease models.
                <sup>
					
                    <xref ref-type="bibr" rid="ref7">7</xref>
				</sup> GSK-3 plays a very important key role in the metabolic process and regulating structural processes in adult neurons as well as in developing neurons.
                <sup>
					
                    <xref ref-type="bibr" rid="ref8">8</xref>
				</sup> In the present study, we used a molecular modeling approach. For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds.</p>
        </sec>
        <sec id="sec6" sec-type="methods">
            <title>Methods</title>
            <sec id="sec7">
                <title>Software used for this computational study</title>
                <p>We used Maestro for our computational simulation studies; the graphical interface was Schr&#x00f6;dinger. In the present work, we used different modules that were used in previous studies.: Protein Preparation Wizard, Ligand Preparation with the help of LigPrep, for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, we used Qikprop, Docking studies we used the Glide module, and binding energy prediction we used Prime and Molecular dynamic simulation studies by Desmond.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref9">9</xref>
					</sup>
					
                    <sup>&#x2013;</sup>
					
                    <sup>
						
                        <xref ref-type="bibr" rid="ref11">11</xref>
					</sup> Alternatively freely available software are AutoDock 4, ArgusLab, and Gromacs.</p>
            </sec>
            <sec id="sec8">
                <title>Selection and preparation of protein</title>
                <p>We used PDB I.D: 6Y9R (
                    <ext-link ext-link-type="uri" xlink:href="http://www.rcsb.org/">http://www.rcsb.org/</ext-link>)
                    <sup>
						
                        <xref ref-type="bibr" rid="ref12">12</xref>
					</sup> of GSK-3&#x03b2;, which is bound to a co-crystallized ligand with a Resolution of 2.08 &#x00c5; consisting of only chain A, downloaded from the protein databank site,
                    <sup>
						
                        <xref ref-type="bibr" rid="ref10">10</xref>
					</sup>
					
                    <sup>,</sup>
					
                    <sup>
						
                        <xref ref-type="bibr" rid="ref11">11</xref>
					</sup> and imported this PDB I. D to the Maestro interface. This is followed by preparing the protein molecule with the help of the protein preparation wizard panel of the Schr&#x00f6;dinger suite. Alternatively, the AutoDock 4 which is freely available software can be used. The first step for protein preparation is preprocessing in the workspace the protein structure is selected then Assigned bond orders, using the CCD database, explicit hydrogen to the structure, zero-order bonds to metals, creating disulfide bonds, and optimizing missing side chain atoms by running a Prime job, fill in missing loops by running the Prime job was selected and delete waters that are further than specified distance beyond 5.00 &#x00c5; from any of the het groups including ions. This is recommended for Glide and virtual screening, but Molecular Dynamics applications should keep these water as they will help the equilibrate the solvent box faster and generate het states using Epik pH: 7.5&#x00b1;0.0, and clicked for preprocessing. The second step is to review and modify here when a new chain, water, or het is selected to zoom to fit the selection to the workspace, select waters and hets within 5.0 &#x00c5; of selected chains, and keep the remaining residues and chains as the default setting and generate a state pH of 7.5&#x00b1;0.0. The third step is to refine we have selected sample water orientations in addition to other groups, and the protonation states of residues and ligands were set by using PROPKA pH: 7.5 then it simulated the exact experimental conditions and clicked to automatically optimize hydroxyl, Asn, Gln, His states using ProtAssign, removing water that is further than beyond hets 3.0 &#x00c5;, including ions. This is recommended for Glide and virtual screening, but Molecular Dynamics applications should keep this water as it will help equilibrate the solvent box faster. We used the OPLS3e force field for the restrained minimization of proteins. After minimization, the glycerol and acetate ions were removed from the minimized protein.</p>
            </sec>
            <sec id="sec9">
                <title>Ligand selection and preparation</title>
                <p>Imidazo [1,5-a]pyridine-3-carboxamide (IMID 2) scaffolds were used for this study. This scaffold possesses fewer hydrogen bond donors and improved Central Nervous System (CNS) penetration at micromolar concentrations (&#x03bc;M), based on studies by Buonfiglio et al.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref13">13</xref>
					</sup> To generate the focus library, we first drew an imidazo[1,5-a]pyridine-3-carboxamide (IMID 2) scaffold structure at Enamine and subjected it to a substructure search. Then, we obtained enamine realdb_IMID2_Scaffold_molecules (1400 molecules), followed by ligand preparation using LigPrep in the Schr&#x00f6;dinger suite software (Schr&#x00f6;dinger 2021-3).
                    <sup>
						
                        <xref ref-type="bibr" rid="ref14">14</xref>
					</sup> Alternatively, the AutoDock 4 which is freely available software can be used. The 3D coordinates for the compounds were generated using the LigPrep tool, and the Epik module predicted the most probable charge form for the ionization state of compounds at pH: 7.5&#x00b1;0.0, generated tautomers and stereoisomers, determined chiralities from the 3D structure, generated at most 32 per ligand, and finally, the minimization of the drug molecule process was performed using the force field OPLS3e.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref15">15</xref>
					</sup>
					
                    <sup>,</sup>
					
                    <sup>
						
                        <xref ref-type="bibr" rid="ref16">16</xref>
					</sup>
				</p>
            </sec>
            <sec id="sec10">
                <title>ADME prediction</title>
                <p>After ligand preparation, all ligands were subjected to Qikprop in the Schr&#x00f6;dinger suite software to predict the ADME profile of the compounds. QikProp predicts the drug-like properties of all selected compounds, such as molecular weight, Hydrogen Bond Donors, Hydrogen Bond Acceptor, QPlogS (predicted aqueous solubility), QPlogPo/w (predicted octanol/water partition coefficient), QPlogBB (predicted brain/blood partition coefficient), QPlogPw (predicted water/gas partition coefficient), QPlogPC16 (predicted hexadecane/gas partition coefficient), QPlogPoct (predicted octanol/gas partition coefficient), PSA (Van der Waals surface area of polar nitrogen and oxygen atoms), QPPCaco (predicted apparent Caco-2 cell permeability), and QPPMDCK (predicted apparent MDCK cell permeability).
                    <sup>
						
                        <xref ref-type="bibr" rid="ref17">17</xref>
					</sup>
				</p>
            </sec>
            <sec id="sec11">
                <title>Molecular docking studies</title>
                <p>Here, we used the Schr&#x00f6;dinger suite Glide module to better understand the prediction of the protein-ligand involved in assessing the fitting of all conformations of compounds at the binding site followed by ranking and modes Schr&#x00f6;dinger Release 2021-3. The alternative freely accessible softwares are AutoDock and Gromacs. The molecular docking study process was used for assessment before molecular docking studies, and the default settings were used for the Receptor Grid generation module. The binding site was recognized using the Receptor Grid generation module, and this binding site was specified as a box of 10 &#x00d7; 10 &#x00d7; 10 &#x00c5;
                    <sup>3</sup> centered on the centroid of the co-crystallized ligand of GSK-3&#x03b2;, which was taken into consideration for grid generation.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref18">18</xref>
					</sup> We subjected all prepared compounds to the standard precision (SP) scoring function mode.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref19">19</xref>
					</sup> Glide SP docking ligand sampling is in flexible mode and selected sample nitrogen inversions, sample ring conformations, and amides only penalize non-planar conformations, adding Epik state penalties to the docking score. In the output file, we selected the pose viewer file that includes the receptor, written out at most one pose per ligand, performed post-docking minimization, the number of poses per ligand to include was five, and the RMSD was computed to input ligand geometries and run the job. Finally, the results were analyzed, separated, and selected based on the VAL135 residue interaction and physicochemical properties reported in the literature. These compounds were then subjected to the prime-MMGBSA module.</p>
            </sec>
            <sec id="sec12">
                <title>MM-GBSA binding energy</title>
                <p>MM-GBSA (molecular mechanics-generalized born energies &amp; surface area/accessibility continuum solvation method) was used to calculate the binding free energy and binding analysis of 523 SP docked protein-ligand complexes. MM-GBSA exhibits many energy properties for proteins, ligands, and complex structures. Prime MM-GBSA analysis is based on the solvent model of VSGB 2.0, and the OPLS3e force field is used to calculate the binding affinity of the respective protein-ligand complex.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref20">20</xref>
					</sup>
				</p>
            </sec>
            <sec id="sec13">
                <title>Molecular dynamics simulation with desmond</title>
                <p>This model was used to precisely predict the interaction of the ligand with 6Y9R (protein), the stability of their binding under physiological conditions, and to analyze their motion at the molecular and atomic levels. The protein-ligand complex was subjected to a system built here, and different solvent models were selected; an orthorhombic box with dimensions of 10 &#x00c5; &#x00d7; 10 &#x00c5; &#x00d7; 10 &#x00c5; was used to determine the structure geometry to have the minimum box volume, and the checked boundary box is shown in the workspace. The next step is to load the previous system builder file and enter 100ns in simulation time, 1000frames were captured, ensemble class is NPT (Normal Pressure Temperature), 300&#x00b0;K temperature, pressure bar is 1, and relax model system before simulation was used for molecular dynamic simulation studies.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref21">21</xref>
					</sup> After Molecular dynamic simulation, the analysis was done with the help of the RMSD (Root Mean Square Deviation), the RMSD of ligand, RMSD of protein. RMSD is mainly used to determine the stability of the protein-ligand complex, RMSF (Root Mean Square Fluctuations), ligand-protein contacts, and ligand interaction diagram. Alternatively, NAMD molecular simulation along with VMD software can be used which is freely available.</p>
            </sec>
        </sec>
        <sec id="sec14" sec-type="results|discussion">
            <title>Results and Discussion</title>
            <p>When we searched for PDB in UniProt, we identified 89 GSK-3&#x03b2; human PDBs. out of 89 PDB entries, 8 PDB literature are yet to be published, and some PDBs with position 3-12 amino acids residual chains C and D are removed from my PDB selection list. PDBs that have chain A/B are taken into consideration, and we studied all the PDBs individually. Finally, we decided on PDB: 6Y9R.
                <sup>
					
                    <xref ref-type="bibr" rid="ref25">25</xref>
				</sup>
			</p>
            <p>The co-crystal structure of 2 was in complex with PDB I. D 6Y9R, which is a GSK-3&#x03b2; enzyme. While INDZ was located at the hinge region, the piperidine chain was exposed to the solvent towards ARG141, Pyridine formed a hydrogen bond with LYS85, nearer to the ligand several hydration sites with residues such as THR138, ASN186, GLN185, LEU132, ASP200, and IIE62 were found to be fully explained in a previous study. The INDZ core scaffold was located in the ATP-competitive binding site of GSK-3&#x03b2; between the N-terminal lobe and the C-terminal lobe region of the protein, and INDZ was previously developed as a novel GSK-3&#x03b2; inhibitor using computational tools. The N-2 of the core and VAL135 N-H group, hydrogen at position N-1 of the core with the ASP133 carbonyl group, hydrogen of the carboxamide group, and VAL135 carbonyl group are important interactions. The R1 substituent appended to the core is oriented towards ARG141 in the external solvent-accessible part of the solvent, and the R2 substituent appended to the LYS85 inner cavity proximity. The indazole N-Ha was then replaced with the C-Ha group, and the ring nitrogen atoms were shuffled, resulting in the IMID2 core.
                <sup>
					
                    <xref ref-type="bibr" rid="ref13">13</xref>
				</sup>
			</p>
            <p>The docked ligands accommodated in the ATP-binding site are similar to the co-crystal structure of the 6Y9R protein. The overlap of the co-crystal structures of the 6Y9R and IMID2 substructures is shown in 
                <xref ref-type="fig" rid="f1">Figure 1</xref> (
                <xref ref-type="fig" rid="f1">Figure 1A</xref> to 
                <xref ref-type="fig" rid="f1">1I</xref>).</p>
            <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                <label>Figure 1. </label>
                <caption>
                    <title>Overlap of co-crystal structure of 6Y9R and IMID2 substructures.</title>
                </caption>
                <graphic id="gr1a" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure1.gif"/>
            </fig>
            <p>The docking of ligands to a particular receptor grid generated a region of the protein 6Y9R. In this study, SP docking was used. After docking, the compounds were separated and selected based on the VAL135 residue interaction in the literature. Therefore, based on the VAL135 N-H group {the H group of the VAL135 protein should show direct interaction with the nitrogen group of the ligand}. Through this process, we finalized 523 molecules of the IMID2 scaffold. Compounds with good SP-scores are listed in 
                <xref ref-type="table" rid="T1">Table 1</xref>.</p>
            <table-wrap id="T1" orientation="portrait" position="float">
                <label>Table 1. </label>
                <caption>
                    <title>Intermolecular interactions with amino acids and docking score of the top 9 selected compounds.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Name</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Docking score</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Structure</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Bonded interaction</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Non-bonded interaction</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z3336252116</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-8.535</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA01.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135,</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ILE62, ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, ALA83, VAL110, LYS85, CYS199, VAL70</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z3136198649</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-8.285</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA02.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ILE62, ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, ALA83, VAL110, LYS85, CYS199, VAL70</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">PV-005996498401</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-8.132</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA03.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARG141, THR138, GLU137, PRO136, LEU189, LEU188, TYR134, ASP133, LEU132, VAL110, ALA83, LYS85, CYS199, VAL70, ILE62</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">PV-0025895352867</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-7.541</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA04.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135, PRO136
                                <break/>PI-cation: ARG141</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">VAL110, ALA83, LYS85, CYS199, VAL70, ILE62, LEU132, ASP133, TYR134, LEU188, GLU137, THR138</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z3603284828</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-7.765</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA05.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ILE62, ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, ALA83, VAL110, LYS85, CYS199, VAL70</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z3136169169</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-8.268</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA06.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARG141, THR138, GLU137, PRO136, LEU189, LEU188, TYR134, ASP133, LEU132, VAL110, ALA83, LYS85, CYS199, VAL70, ILE62</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z2497631540</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-7.967</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA07.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, VAL110, CYS199, ALA83, LYS85, VAL70, ILE62</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z4468779454</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-7.306</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA08.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, VAL110, CYS199, ALA83, LYS85, VAL70, ILE62</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Z2708235883</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">-7.961</td>
                            <td colspan="1" rowspan="1">
								
                                <inline-graphic xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_GRA09.gif"/>
							</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">H-bond: VAL135</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, VAL110, CYS199, ALA83, LYS85, VAL70, ILE62</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
            <sec id="sec15">
                <title>ADME prediction</title>
                <p>The Absorption, Distribution, Metabolism, and Excretion (ADME) or drug-likeness parameters are the most important and helpful parameters for screening compounds after SP docking studies during the drug discovery process.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref22">22</xref>
					</sup> Generally, for CNS drugs, the physicochemical properties range from molecular weight 100 Da to 450 Da, hydrogen bond acceptors range from 0 to 5, hydrogen bond donor values range from 0 to 3, and the topological polar surface area value is &#x2264;76 &#x00c5;2.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref23">23</xref>
					</sup> The results of this study are listed in 
                    <xref ref-type="table" rid="T2">Table 2</xref>. Whereas the previous ADME properties of 13 trial compounds with an IMID2 core scaffold molecular weight of 393.48, the hydrogen bond donor was 1. In the14 trial compound of the IMID2 core scaffold, the molecular weight was 451.56, and the hydrogen bond donors were 1. In the 15 trial compounds of the IMID2 core scaffold, the molecular weight was 336.39, and the hydrogen bond donors were 1. In 16 trials of the IMID2 core scaffold, the molecular weight was 394.47, and the number of hydrogen bond donors was 1.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref13">13</xref>
					</sup>
				</p>
                <table-wrap id="T2" orientation="portrait" position="float">
                    <label>Table 2. </label>
                    <caption>
                        <title>MM-GBSA score and Qikprop ADME prediction results of the top-9 selected compounds.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Name</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">MM-GBSA score</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Molecular weight</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Hydrogen Bond Donor</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Hydrogen Bond Acceptor</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Polar Surface Area</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z3336252116</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-36.94</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">252.660</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">105.021</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z3136198649</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-40.67</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">232.243</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">81.022</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PV-005996498401</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-33.07</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">232.243</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">101.470</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PV-002589535286</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-37.94</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">241.252</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.5</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">78.034</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z3603284828</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-40.91</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">245.282</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">72.320</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z3136169169</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-40.53</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">232.243</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">81.001</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z2497631540</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-42.12</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">253.688</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.7</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">73.124</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z4468779454</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-46.04</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">247.296</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.7</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">70.083</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">Z2708235883</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">-39.16</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">237.233</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.7</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">73.152</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
            </sec>
            <sec id="sec16">
                <title>Molecular dynamic simulation analysis</title>
                <p>Compounds were selected based on the screening process described above. Only short-listed compounds were subjected to desmonds for molecular dynamic simulations. These studies provide information related to the stability and flexibility of the molecular docking complexes. We performed this study using the MM-GBSA. This study provides information related to protein-ligand interactions, RMSD variation can be assessed, and RMSF fluctuations can be assessed for protein-ligand complexes. The complex was regarded as stable if it fell within the 3 &#x00c5; range. These are the compounds</p>
                <p>
					
                    <bold>IMID2 PV-002589535286:</bold> During MD simulations, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20nsec. The protein backbone and ligand fluctuations stayed within the range of 0.6 &#x00c5; and 1.3 &#x00c5; over the last 80nsec. The amino acid residue VAL135 formed a 95% direct hydrogen bonding interaction with the amide carbonyl, the amino acid residue VAL135 formed 42% direct hydrogen bonding interaction with imidazole in the ring structure, the amino acid residue TYR134 had 33% direct hydrogen bonding interaction, and ARG141 had a 72% PI-cation interaction with 1H-imidazole. The amide carbonyl was exposed to an H
                    <sub>2</sub>O molecule through which it interacted with residue ILE62 (49%), as shown in 
                    <xref ref-type="fig" rid="f2">Figure 2</xref>; however, it was reported in 
                    <bold>13</bold> triads (previous literature) that the amide carbonyl was exposed to water-mediated contacts with residues such as ILE62 and GLN185 for the IMID2 core scaffold. The 16-triad isopropoxyl group confirmed the probable water molecule interaction with the residue ASP200. The 
                    <bold>17</bold> triads showed an indirect interaction between the ortho substituent and residue ASN186. In the 18 triad, the di-F phenyl group was orientated towards LYS85, thus promoting an electrostatic interaction. However, VAL135 interaction formed direct hydrogen bonding interactions with amide carbonyl in all 13, 16, 17, and 18 trial compounds of previous IMID2 scaffolds.
                    <sup>
						
                        <xref ref-type="bibr" rid="ref13">13</xref>
					</sup>
				</p>
                <fig fig-type="figure" id="f2" orientation="portrait" position="float">
                    <label>Figure 2. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interactions with the IMID2 compound code PV-002589535286 monitored during the course of MD Simulation trajectory (A) Protein-Ligands RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure2.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code PV-002589535286 was observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues were in greater contact with ligands in the trajectory frame that appeared as a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code Z3603284828:</bold> During the MD simulations, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations during the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.3 &#x00c5; and 0.6 &#x00c5; over the last 70 nsec shown in 
                    <xref ref-type="fig" rid="f3">Figure 3</xref>. The amino acid residue VAL135 formed 93% direct hydrogen bonding interaction with the amide carbonyl, the amino acid residue VAL135 formed 55% direct hydrogen bonding interaction with imidazole, and the amino acid residue ILE62 formed 35% water-mediated direct hydrogen bonding interaction with 1-hydroxycyclopropyl.</p>
                <fig fig-type="figure" id="f3" orientation="portrait" position="float">
                    <label>Figure 3. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code Z3603284828 monitored during the course of MD Simulation trajectory (A) Protein-Ligands RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr3" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure3.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code Z3603284828 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis, a trajectory frame with a number is present, and amino acid residues are seen on the y-axis. The amino acid residues that were in greater contact with ligands in the trajectory frame appeared as a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code Z3136169169:</bold> During the MD simulations, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations during the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.8 &#x00c5; and 0.3 &#x00c5; over the last 70 nsec shown in 
                    <xref ref-type="fig" rid="f4">Figure 4</xref>. The amide carbonyl is exposed to hydrogen bonding and interacts with residue VAL135, and the amino acid residue VAL135 forms 76% direct hydrogen bonding interactions with imidazole.</p>
                <fig fig-type="figure" id="f4" orientation="portrait" position="float">
                    <label>Figure 4. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code Z3136169169 monitored during the course of Molecular Dynamic Simulation trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligands Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr4" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure4.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code Z3136169169 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that were in greater contact with ligands in the trajectory frame appeared as a dark color shade). The VAL135 interaction fraction approached 1.6, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature and protein RMSF is mostly helpful for predicting the changes that occur locally along with the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code Z2497631540:</bold> During the MD simulations, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein C&#x03b1; and ligand structures exhibited higher RMSD fluctuations over the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.4 &#x00c5; and 3.7 &#x00c5; over the last 70 nsec shown in 
                    <xref ref-type="fig" rid="f5">Figure 5</xref>. The amino acid residue VAL135 formed 84% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 39% direct hydrogen bonding interactions with imidazole in the ring structure.</p>
                <fig fig-type="figure" id="f5" orientation="portrait" position="float">
                    <label>Figure 5. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code Z2497631540 monitored during the course of MD Simulation trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr5" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure5.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code Z2497631540 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis, a trajectory frame with a number is present, and amino acid residues are seen on the y-axis. The amino acid residues that were in greater contact with ligands in the trajectory frame appeared as a dark color shade). The VAL135 interaction fraction approached 1.2, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code Z4468779454:</bold> During the MD simulations, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein C&#x03b1; and ligand structures exhibited higher RMSD fluctuations over the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.5 &#x00c5; and 0.4 &#x00c5; over the last 70 nsec shown in 
                    <xref ref-type="fig" rid="f6">Figure 6</xref>. The amino acid residue VAL135 formed a 96% direct hydrogen bonding interaction with the amide carbonyl, the amino acid residue VAL135 formed a 77% direct hydrogen bonding interaction with imidazole, and the amide carbonyl was exposed to the H
                    <sub>2</sub>O molecule through which it interacted with residue ILE62, with 43% and 61% interaction with 1-hydroxypentan-2-yl.</p>
                <fig fig-type="figure" id="f6" orientation="portrait" position="float">
                    <label>Figure 6. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code Z4468779454 monitored during the course of Molecular Dynamic Simulation trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand contacts; (C) Ligand-Protein contacts; (D) Protein-Ligand contacts described as histogram; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr6" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure6.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code Z4468779454 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues were in greater contact with ligands in the trajectory frame that appeared as a dark color shade). The VAL135 interaction fraction approached 1.75, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code Z2708235883:</bold> During the MD simulations, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations during the first 20 ns. The protein C&#x03b1; and ligand fluctuations stayed within the range of 0.5 &#x00c5; and 0.2 &#x00c5; over the last 70 nsec. The amino acid residue VAL135 formed 93% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 53% direct hydrogen bonding interactions with imidazole in the ring structure, as shown in 
                    <xref ref-type="fig" rid="f7">Figure 7</xref>.</p>
                <fig fig-type="figure" id="f7" orientation="portrait" position="float">
                    <label>Figure 7. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code Z2708235883 monitored during the course of MD Simulation trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr7" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure7.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code Z2708235883 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
            </sec>
            <sec id="sec22">
                <title/>
                <p>
					
                    <bold>IMID2_compound code Z3336252116</bold>: During MD simulations, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20nsec. The protein backbone and ligand fluctuations stayed within the range of 0.6 &#x00c5; and 2.2 &#x00c5; over the last 80nsec. The amino acid residue VAL135 formed 93% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 48% direct hydrogen bonding interactions with imidazole in the ring structure, which was exposed to the H
                    <sub>2</sub>O molecule through which it interacted with residue ILE62 (43%), as shown in 
                    <xref ref-type="fig" rid="f8">Figure 8</xref>.</p>
                <fig fig-type="figure" id="f8" orientation="portrait" position="float">
                    <label>Figure 8. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code Z3336252116 monitored during the course of MD Simulation trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand Contacts; (C) Protein-Ligand Contacts described as histogram; (D) Ligand-Protein Contacts; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr8" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure8.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2_compound code Z3336252116 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.5, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein RMSF is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code Z3136198649</bold>: During MD simulations, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20nsec. The protein backbone and ligand fluctuations stayed within the range of 0.7 &#x00c5; and 0.5 &#x00c5; over the last 80nsec. The amino acid residue VAL135 formed 83% direct hydrogen bonding interaction with amide carbonyl, and the amino acid residue VAL135 formed 49% direct hydrogen bonding interaction with imidazole in the ring structure given in 
                    <xref ref-type="fig" rid="f9">Figure 9</xref>.</p>
                <fig fig-type="figure" id="f9" orientation="portrait" position="float">
                    <label>Figure 9. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interactions with the IMID2 compound code Z3136198649 monitored during the course of Molecular Dynamic Simulation trajectory (A) Protein-Ligands RMSD; (B) Protein-Ligand Contacts; (C) Protein-Ligand Contacts described as histogram; (D) Ligand-Protein Contacts; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr9" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure9.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R with IMID2 compound code Z3136198649 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein RMSF is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
                <p>
					
                    <bold>IMID2 compound code PV-005996498401</bold>: During MD simulations, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20nsec. The protein backbone and ligand fluctuations stayed within the range of 1.0 &#x00c5; and 0.6 &#x00c5; over the last 80 nsec. The amino acid residue VAL135 formed 90% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 47% direct hydrogen bonding interactions with imidazole in the ring structure, as shown in 
                    <xref ref-type="fig" rid="f10">Figure 10</xref>.</p>
                <fig fig-type="figure" id="f10" orientation="portrait" position="float">
                    <label>Figure 10. </label>
                    <caption>
                        <title>Pictorial representation of 6Y9R interaction with the IMID2 compound code PV-005996498401 monitored during the course of Molecular Dynamic Simulation trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand contacts; (C) Protein-Ligand contacts described as histogram; (D) Ligand-Protein contacts; (E) Protein RMSF.</title>
                    </caption>
                    <graphic id="gr10" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/170347/d44e8f42-bff3-40f4-970a-d5c1f2547953_figure10.gif"/>
                </fig>
                <p>Interaction diagram of 6Y9R withIMID2 compound code PV-005996498401 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during Molecular dynamics simulation, protein&#x2013;ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.85, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein RMSF is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the molecular dynamic simulation study.</p>
            </sec>
        </sec>
        <sec id="sec25" sec-type="conclusion">
            <title>Conclusion</title>
            <p>In this study, we used focused library generation to target the receptor grid region (ATP-competitive site) of 6Y9R. After docking, the compounds were separated and selected based on the VAL135 residue interaction. Further prediction was performed using Qikprop and Prime MM-GBSA assays and Molecular dynamic simulation studies. Further experimental studies are required to confirm these findings.</p>
            <sec id="sec26">
                <title>Ethical considerations</title>
                <p>Ethics and written consent were not applicable.</p>
            </sec>
        </sec>
    </body>
    <back>
        <sec id="sec29" sec-type="data-availability">
            <title>Data availability</title>
            <p>Figshare: Molecular docking studies and molecular dynamic simulation to identify GSK-3&#x03b2; inhibitors for Alzheimer&#x2019;s disease, 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.24592716.v1">https://doi.org/10.6084/m9.figshare.24592716.v1</ext-link>.
                <sup>
					
                    <xref ref-type="bibr" rid="ref24">24</xref>
				</sup>
			</p>
            <p>The underlying data for this project are:
                <list list-type="bullet">
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
Enamine_IMID2_Scaffold.csv</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
Enamine_IMID2_Scaffold_with nitrogen_realdb_molecules 17_35_52.sdf</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
Ligprep_enamine_IMID2_Scaffold_withnitrogen_realdb_out.csv</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
Ligprep_enamine_IMID2_SCAFFOLD_WITHNITROGEN_REALDB-out.mae</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
Prime_mmgbsa_3_out.csv</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
prime_mmgbsa_3-out.maegz</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
Prime_mmgbsa_VAL135_INTERACTION_IMID2_ENAMINE_WITHNITROGEN-out.csv</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
prime_mmgbsa_VAL135_INTERACTION_IMID2_ENAMINE_WITHNITROGEN-out.maegz</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
QIKPROP_LIGPRE_IMID2_ENAMINE_WITHNITROGEN-out.csv</p>
                    </list-item>
                    <list-item>
                        <label>&#x27a2;</label>
                        <p>
QIKPROP_LIGPREP_ENAMINE_IMID2_SCAFFOLD_WITHNITROGEN_REALDB-out.mae</p>
                    </list-item>
                </list>
			</p>
            <p>Data are available under the terms of the 
                <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International license</ext-link> (CC-BY 4.0).</p>
            <p>Protein Data Bank: 6Y9R, 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.2210/pdb6Y9R/pdb">https://doi.org/10.2210/pdb6Y9R/pdb</ext-link>.
                <sup>
					
                    <xref ref-type="bibr" rid="ref25">25</xref>
				</sup>
			</p>
        </sec>
        <ack>
            <title>Acknowledgements</title>
            <p>The authors would like to thank the Indian Council of Medical Research (ICMR), New Delhi, India, for the ICMR-SRF (#2021-11506_F1) to Suggala Ramya Shri.</p>
        </ack>
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    <sub-article article-type="reviewer-report" id="report318530">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.170347.r318530</article-id>
            <title-group>
                <article-title>Reviewer response for version 2</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Rai</surname>
                        <given-names>Sachchida</given-names>
                    </name>
                    <xref ref-type="aff" rid="r318530a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-8418-9549</uri>
                </contrib>
                <contrib contrib-type="author">
                    <name>
                        <surname>Singh</surname>
                        <given-names>Payal</given-names>
                    </name>
                    <xref ref-type="aff" rid="r318530a2">2</xref>
                    <role>Co-referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-4836-341X</uri>
                </contrib>
                <aff id="r318530a1">
                    <label>1</label>Centre of Experimental Medicine and Surgery (CEMS), Institute of Medical Sciences (IMS), Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India</aff>
                <aff id="r318530a2">
                    <label>2</label>Department of Zoology, Banaras Hindu University, Varanasi, Uttar Pradesh, India</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>28</day>
                <month>9</month>
                <year>2024</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Rai S and Singh P</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport318530" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.145391.2"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>I have some questions on the manuscript which are as follows.</bold>
            </p>
            <p> How do the identified GSK-3&#x03b2; inhibitors compare to existing therapeutics in terms of binding affinity and specificity?</p>
            <p> What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy?</p>
            <p> Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered?</p>
            <p> How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies?</p>
            <p> Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study?</p>
            <p> Have you considered testing the identified GSK-3&#x03b2; inhibitors in relevant Alzheimer&#x2019;s disease animal models?</p>
            <p> Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time?</p>
            <p> How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline?</p>
            <p> Was there any off-target effects observed for the identified inhibitors in the computational analyses?</p>
            <p> Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), &amp; Rai SN, et al., 2020 (Ref 6) in your manuscript.</p>
            <p> Could targeting GSK-3&#x03b2; alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach?</p>
            <p> How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R?</p>
            <p> What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity?</p>
            <p> How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation?</p>
            <p> Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes?</p>
            <p> Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency?</p>
            <p> How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations?</p>
            <p> What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process?</p>
            <p> How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity?</p>
            <p> Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding?</p>
            <p> How do you plan to prioritize the compounds for further experimental testing, given the computational predictions?</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Yes</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>My research focuses on exploring the molecular mechanisms and therapeutic interventions for neurodegenerative diseases, particularly Alzheimer's and Parkinson's disease. I investigate the roles of biomarkers, signaling pathways, and epigenetic regulators in disease progression. My work integrates computational biology, drug discovery, and experimental models to identify novel therapeutic targets, including GSK-3&#x03b2; and other key enzymes. I also explore natural compounds, such as those derived from nutraceuticals and endophytic fungi, for their neuroprotective potential. Additionally, I study autophagy, oxidative stress, and inflammation as critical factors in neurodegeneration, aiming to develop targeted strategies for chronic disease mitigation and brain health.</p>
            <p>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.</p>
        </body>
        <back>
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        <sub-article article-type="response" id="comment13919-318530">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Ranganath Pai</surname>
                            <given-names>Sreedhara</given-names>
                        </name>
                        <aff>Manipal college of pharmaceutical sciences, India</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>Authors declare that there is no competing interest.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>16</day>
                    <month>5</month>
                    <year>2025</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Comment 1.</bold> How do the identified GSK-3&#x03b2; inhibitors compare to existing therapeutics in terms of binding affinity and specificity?</p>
                <p> 
                    <bold>Answer to the comment: </bold>The binding affinity and specificity are generally related in that high binding affinity usually leads to high specificity for a particular target. The binding affinity and specificity of GSK-3&#x03b2; inhibitors vary depending on the type of inhibitors/ lignads used.</p>
                <p> </p>
                <p> 
                    <bold>Comment 2.</bold> What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy?</p>
                <p> 
                    <bold>Answer to the comment:</bold> Instead of a similarity structure in this study, substructure search was used for IMID2 scaffold at Enamine and the compounds were subjected to Qikprop to predict the accuracy of the compounds to cross the BBB (QPlogBB). In a previous study, QPlogBB was not used, whereas other physicochemical parameters were used, such as molecular weight, hydrogen bond donor and hydrogen bond acceptor.
                    <bold>&#x00a0; </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 3.</bold> Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered?</p>
                <p> 
                    <bold>Answer to the comment: </bold>In this study, the substructure compound search was used for ligand identification. All the compounds used in this study were novel, but scaffold was similar to previous literature.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 4.</bold> How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies?</p>
                <p> 
                    <bold>Answer to the comment: </bold>Absorption is how the drugs go through the organs of the body to reach the systemic circulation. Distribution of the drug/compound transported from one tissue to another tissue or from one organ to another organ. The transportation or distribution of compounds/drugs into the brain/CNS is the main focus in drug discovery.&#x00a0; The metabolism is also referred to as the biotransformation of exogenous compounds/drugs to increase their water solubility and hydrophilicity. Finally, the water solubility facilitates their excretion process. Using the information to evaluate the compound's drug safety and risk outcomes.</p>
                <p> </p>
                <p> 
                    <bold>Comment 5.</bold> Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study?</p>
                <p> 
                    <bold>Answer to the comment: </bold>As the computation methods are regularly and routinely used, there are no challenges encountered in using the computational simulation tools.</p>
                <p> </p>
                <p> 
                    <bold>Comment 6.</bold> Have you considered testing the identified GSK-3&#x03b2; inhibitors in relevant Alzheimer&#x2019;s disease animal models?</p>
                <p> 
                    <bold>Answer to the comment: </bold>No, yet to consider testing the identified GSK-3&#x03b2; inhibitors in relevant Alzheimer&#x2019;s disease animal models.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 7.</bold> Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time?</p>
                <p> 
                    <bold>Answer to the comment: </bold>Yes, the molecular dynamics (MD) simulations can provide insights into the stability and conformational changes of protein-ligand complexes over time. MD simulation studies predict how protein-ligand interactions occur, analyse dynamic changes and conformational changes in the proteins.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 8.</bold> How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline?</p>
                <p> 
                    <bold>Answer to the comment: </bold>The finalized compounds will be subjected to in vitro and in vivo studies. If the molecules are showing good results, they can be translated into clinical studies.</p>
                <p> </p>
                <p> 
                    <bold>Comment 9.</bold> Was there any off-target effects observed for the identified inhibitors in the computational analyses?</p>
                <p> 
                    <bold>Answer to the comment: </bold>No such off-target effects were observed for inhibitors in computational analyses using the Schrodinger Maestro tool.</p>
                <p> </p>
                <p> 
                    <bold>Comment 10.</bold> Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), &amp; Rai SN, et al., 2020 (Ref 6) in your manuscript.</p>
                <p> 
                    <bold>Answer to the comment: </bold>These references are not related to our objectives, hence these are not included as relevant references.</p>
                <p> </p>
                <p> 
                    <bold>Comment 11.</bold> Could targeting GSK-3&#x03b2; alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach?</p>
                <p> 
                    <bold>Answer to the comment: </bold>As most of the current study focuses on targeting GSK-3&#x03b2;, and the preclinical data to a greater extent prevent Alzheimer&#x2019;s disease, however, the question of whether GSK-3&#x03b2; alone is sufficient to address the complex pathology of Alzheimer's disease remains to be answered through clinical investigations.
                    <bold>&#x00a0;&#x00a0; </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 12.</bold> How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R?</p>
                <p> 
                    <bold>Answer to the comment:</bold> The identified GSK-3&#x03b2; inhibitors particularly bind to the specific ATP-competitive site in 6Y9R protein region and reduce the off-target effects.
                    <bold>&#x00a0;&#x00a0; </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 13.</bold> What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity?</p>
                <p> 
                    <bold>Answer to the comment: </bold>This was based on previous literature, in this study N-region of the core and VAL135 at N-H group of the ligand.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 14.</bold> How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation?</p>
                <p> 
                    <bold>Answer to the comment: </bold>Qikprop: Predicts pharmaceutically relevant properties of the compounds and physically significant descriptors of an individual compound. Prime MM-GBSA: Predicts the protein-ligand binding free energy of an individual compound. This was accurate to estimate the relative binding free energy of the compound/ligand.</p>
                <p> </p>
                <p> 
                    <bold>Comment 15.</bold> Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes?</p>
                <p> 
                    <bold>Answer to the comment: </bold>We have elaborated this part in the revised manuscript.</p>
                <p> </p>
                <p> 
                    <bold>Comment 16.</bold> Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency?</p>
                <p> 
                    <bold>Answer to the comment: </bold>The alternative residue, PRO 136, forms a hydrogen bond interaction with the core of IMID2.</p>
                <p> </p>
                <p> 
                    <bold>Comment 17.</bold> How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations?</p>
                <p> 
                    <bold>Answer to the comment: </bold>The docking score gives an idea about the score of a compound and is used to predict the binding affinity of protein and ligand when it is subjected to a molecular docking study. Qikprop gives an idea about the compound's ability to cross the BBB or not. The MM-GBSA gives an idea about the sum of intermolecular interactions present between the protein and ligand.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 18.</bold> What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process?</p>
                <p> 
                    <bold>Answer to the comment: </bold>In the present study, a molecular docking study was used to subject these compounds to Qikprop, then the compounds which cross the standard range of physicochemical properties. Those compounds were segregated based on VAL135 at N-H group followed by subjecting those compounds to Prime MM-GBSA and Molecular dynamic simulation studies.</p>
                <p> </p>
                <p> 
                    <bold>Comment 19.</bold> How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity?</p>
                <p> 
                    <bold>Answer to the comment: </bold>The identified compounds are better compared to the existing inhibitors of 6Y9R.</p>
                <p> </p>
                <p> 
                    <bold>Comment 20.</bold> Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding?</p>
                <p> 
                    <bold>Answer to the comment: </bold>The interactions during the MD simulation were stable for a larger period, hence, the shortlisted molecules can be better GSK-3&#x03b2; inhibitors.</p>
                <p> </p>
                <p> 
                    <bold>Comment 21.</bold> How do you plan to prioritize the compounds for further experimental testing, given the computational predictions?</p>
                <p> 
                    <bold>Answer to the comment: </bold>Yes, the compounds will be further subjected to in vitro studies and later to in vivo studies.</p>
            </body>
        </sub-article>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report302514">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.159332.r302514</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Bhatt</surname>
                        <given-names>Shvetank</given-names>
                    </name>
                    <xref ref-type="aff" rid="r302514a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r302514a1">
                    <label>1</label>Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>10</day>
                <month>10</month>
                <year>2024</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Bhatt S</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport302514" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.145391.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>The manuscript is well written and can be accepted after minor modifications.</p>
            <p> 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section.</p>
            <p> 2. Results and discussion section is presented well by authors.</p>
            <p> 3. Author should discuss the epidemiology and&#x00a0; pathophysiology of diseases briefly to understand the severity of disease.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>I cannot comment. A qualified statistician is required.</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Yes</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>CNS Diosrders, AD, Depression,&#x00a0; Anxiety</p>
            <p>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.</p>
        </body>
        <sub-article article-type="response" id="comment13920-302514">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Ranganath Pai</surname>
                            <given-names>Sreedhara</given-names>
                        </name>
                        <aff>Manipal college of pharmaceutical sciences, India</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>Authors declare that there is no competing interest.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>16</day>
                    <month>5</month>
                    <year>2025</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Comment 1.</bold> Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section.</p>
                <p> 
                    <bold>Answer to the comment: </bold>The aducanumab, a medication based on the A&#x03b2; theory, in 2021 FDA approved for the treatment of AD. &#x00a0;Lecanemab's effectiveness and safety in treating early-stage AD require longer study trials.</p>
                <p> </p>
                <p> 
                    <bold>Comment 2.</bold> Results and discussion section is presented well by authors.</p>
                <p> 
                    <bold>Answer to the comment: </bold>We thank the reviewer.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 3. </bold>Author should discuss the epidemiology and&#x00a0; pathophysiology of diseases briefly to understand the severity of disease.</p>
                <p> 
                    <bold>Answer to the comment: </bold>AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease's progression, cognitive decline and behavioral changes.</p>
            </body>
        </sub-article>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report302510">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.159332.r302510</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Shah</surname>
                        <given-names>Jigna Samir</given-names>
                    </name>
                    <xref ref-type="aff" rid="r302510a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r302510a1">
                    <label>1</label>Nirma University, Ahmedabad, Gujarat, India</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>13</day>
                <month>9</month>
                <year>2024</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Shah JS</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport302510" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.145391.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>1. Abstract can be modified. Also, in conclusion no specific compound was identified.</p>
            <p> 2. Role of GSK-3&#x03b2; in pathogenesis of AD can be explored more. Mechanism of GSK-3&#x03b2;&#x00a0;in AD can be defined briefly in introduction section.</p>
            <p> 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and</p>
            <p> discussion. It can be elaborated briefly. Also, besides this domain, GSK-3&#x03b2; inhibition&#x00a0;might be possible through different binding sites. If possible, it can also be explored.</p>
            <p> 4. There are grammatical errors and improper sentence formations in manuscript, which&#x00a0;can be modified. Also, instead of using &#x201c;we&#x201d;, the authors can used indirect speech.</p>
            <p> 5. Full forms mentioned are not consistent. Mention the full form in first use and&#x00a0;thereafter abbreviations can be used.</p>
            <p> 6. In result sections, &#x201c;Protein root mean square fluctuation (RMSF) is mostly helpful for&#x00a0;predicting changes that occur locally along the enzyme chain.&#x201d; is repeatedly&#x00a0;mentioned in each result. The authors can mention the significance of findings like&#x00a0;RMSD, RMSF at the start in a separate paragraph, or only once in initial results.&#x00a0;Thereafter it can be understood.</p>
            <p> 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition&#x00a0;coefficient), was predicted however, in results it is not mentioned. It can be&#x00a0;mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter.</p>
            <p> 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can&#x00a0;be taken into consideration.</p>
            <p> 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better&#x00a0;compound should be identified and screened. Biological data to support the findings&#x00a0;can be added, however it is not mandatory. Discussion section should be added and&#x00a0;findings of each compound should be compared and correlated to previously done studies.</p>
            <p> 10. Discuss future prospects of this study, elaborate briefly what experimental studies&#x00a0;should be done further and state the limitations of this study.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Not applicable</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Yes</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Neurodegenerative diseases, oral cancer, breast cancer</p>
            <p>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.</p>
        </body>
        <sub-article article-type="response" id="comment13918-302510">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Ranganath Pai</surname>
                            <given-names>Sreedhara</given-names>
                        </name>
                        <aff>Manipal college of pharmaceutical sciences, India</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>Authors declare that there is no competing interest.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>16</day>
                    <month>5</month>
                    <year>2025</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Comment 1.</bold> Abstract can be modified. Also, in conclusion no specific compound was identified.</p>
                <p> 
                    <bold>Answer to the comment:</bold> Thank you for your comment. The abstract and the conclusion have been modified according to the reviewer's comments in the revised manuscript.</p>
                <p> </p>
                <p> 
                    <bold>Comment 2.</bold> Role of GSK-3&#x03b2; in pathogenesis of AD can be explored more. Mechanism of GSK-3&#x03b2; in AD can be defined briefly in introduction section.</p>
                <p> 
                    <bold>Answer to the comment:</bold> The relevant details were incorporated as per the reviewer's comment in the revised manuscript, page number: 10&#x00a0;</p>
                <p> </p>
                <p> 
                    <bold>Comment 3.</bold> Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3&#x03b2; inhibition might be possible through different binding sites. If possible, it can also be explored.</p>
                <p> 
                    <bold>Answer to the comment:</bold> The PDB ID 6Y9R was released in 2020 for the protein GSK-3&#x03b2;, the amino acid sequence length is proper without any break in sequence, with good resolution and Ramachandran outliers. The SiteMap tool is generally used to explore the different binding sites of a protein. Based on the results from the SiteMap tool, the ATP binding site was explored to identify ATP-competitive inhibitors to explore it in Alzheimer's disease.</p>
                <p> </p>
                <p> 
                    <bold>Comment 4.</bold> There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using &#x201c;we&#x201d;, the authors can used indirect speech.</p>
                <p> 
                    <bold>Answer to the comment:</bold> These errors are corrected in the revised manuscript. The authors are thankful to the reviewer for the suggestions.</p>
                <p> </p>
                <p> 
                    <bold>Comment 5.</bold> Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used.</p>
                <p> 
                    <bold>Answer to the comment:</bold> These are taken care of in the revised manuscript.</p>
                <p> </p>
                <p> 
                    <bold>Comment 6.</bold> In result sections, &#x201c;Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.&#x201d; is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood.</p>
                <p> 
                    <bold>Answer to the comment: </bold>According to the reviewer's comment, the data was added in the revised manuscript, see page number 12.
                    <bold> </bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Comment 7.</bold> In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter.</p>
                <p> 
                    <bold>Answer to the comment: </bold>The data was added in the revised manuscript according to the revised manuscript page number: 10-11.</p>
                <p> </p>
                <p> 
                    <bold>Comment 8.</bold> No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration.</p>
                <p> 
                    <bold>Answer to the comment: </bold>The standard drug or inhibitor are not used for docking a there are no currently available approved drug acting through GSK-3&#x03b2;.</p>
                <p> </p>
                <p> 
                    <bold>Comment 9.</bold> There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies.</p>
                <p> 
                    <bold>Answer to the comment: </bold>In the revised manuscript,
                    <bold> </bold>these points are incorporated as per the reviewer&#x2019;s comments.</p>
                <p> </p>
                <p> 
                    <bold>Comment 10.</bold> Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study.</p>
                <p> 
                    <bold>Answer to the comment: </bold>This point was taken care of in the revised manuscript. The shortlisted compounds can be subjected to in vitro studies to check the cytotoxicity, and based on those study results, the compound can be studied in an animal model to check the safety and efficacy. However, there are limitations to purchasing. The compound, based on the body weight of the animal, requires a larger amount of compound. Further, for synthesis, it needed a lot of time to get the correct scaffold and the required structure.</p>
            </body>
        </sub-article>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report302509">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.159332.r302509</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Zhao</surname>
                        <given-names>Qingchun</given-names>
                    </name>
                    <xref ref-type="aff" rid="r302509a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r302509a1">
                    <label>1</label>Shenyang Pharmaceutical University, Shenyang, China</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>29</day>
                <month>7</month>
                <year>2024</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Zhao Q</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport302509" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.145391.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>reject</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>In this paper, the authors used computer simulation technology to select 9 compounds with GSK3 as the target, and then studied ADME prediction and molecular docking through different modules.</p>
            <p> Keep reading the paper, three questions are raised.</p>
            <p> 1. In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification.</p>
            <p> 2.&#x00a0;In the introduction section, is there only one sentence to summarize the article?</p>
            <p> 3.The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article.</p>
            <p> Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Alzheimer's Disease, Cancer , Natural Pharmaceutical Chemistry, Pharmaceutical chemistry</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.</p>
        </body>
        <sub-article article-type="response" id="comment12214-302509">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Ranganath Pai</surname>
                            <given-names>Sreedhara</given-names>
                        </name>
                        <aff>Manipal college of pharmaceutical sciences, India</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>NIL</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>13</day>
                    <month>8</month>
                    <year>2024</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Reviewer Comment 1:</bold> In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification.</p>
                <p> 
                    <bold>Response to comment 1</bold>: We used Maestro, a graphical interface of Schrodinger, for our computational simulation studies. In the present work, we have used different modules such as Protein Preparation Wizard for Protein Preparation, LigPrep for Ligand Preparation, Qikprop for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, Glide for docking studies, Prime for Binding energy prediction and Desmond for Molecular dynamic simulation studies used.</p>
                <p> </p>
                <p> 
                    <bold>Reviewer Comment 2</bold>: In the introduction section, is there only one sentence to summarize the article?</p>
                <p> 
                    <bold>Response to comment 2</bold>: For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds.</p>
                <p> </p>
                <p> 
                    <bold>Reviewer Comment 3</bold>: The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article.</p>
                <p> Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive.</p>
                <p> 
                    <bold>Response to comment 3</bold>: The reviewer's comment is completely one-sided. We regret to answer this comment, and we completely disagree with the reviewer's suggestion, as this work took 18 months from the conceptualization of the idea. This work is computational modelling, and our objectives are clear, and in our view, the data generated is sufficient to make the conclusion.</p>
            </body>
        </sub-article>
    </sub-article>
</article>
