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Methods: In this study, an integrated computational and experimental approach was employed to identify potential AChE inhibitors. A machine learning-based model was developed to predict bioactive compounds from large chemical libraries, followed by Blood\u2013Brain Barrier (BBB) permeability screening to ensure Central Nervous System (CNS) suitability. The shortlisted compounds were further evaluated using molecular docking, molecular dynamics simulations, and MM-PBSA binding free-energy calculations to assess their interaction profiles and stability. Selected top-ranked compounds were subjected to in vitro biological evaluation for AChE inhibitory activity. Results: The results demonstrated that several screened compounds, including Z1498348710 and Z1824281875, exhibited notable inhibition with AChE activity reduced to approximately 75% and 72%, respectively, compared to the control. Other compounds such as Z29542160, Z105150208, Z94570687, and Z94570675 showed moderate inhibitory effects, maintaining AChE activity in the range of 82\u201386%. In comparison, the reference inhibitors Donepezil and Neostigmine bromide displayed significantly stronger inhibition, reducing AChE activity to approximately 20% and 18%, respectively. Conclusions: Overall, the identified compounds demonstrated moderate AChE inhibitory activity while exhibiting favorable predicted physicochemical and computational profiles. These findings suggest that they represent promising starting points for medicinal chemistry optimization and future development as CNS-active AChE inhibitors.<\/jats:p>","DOI":"10.3390\/ph19071120","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:19:27Z","timestamp":1784564367000},"page":"1120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning and Molecular Modeling Strategy for the Identification of CNS-Active Acetylcholinesterase Inhibitors"],"prefix":"10.3390","volume":"19","author":[{"given":"Muhammad","family":"Yasir","sequence":"first","affiliation":[{"name":"Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7662-5601","authenticated-orcid":false,"given":"Jinyoung","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Taek","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Won Sun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Physiology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-6675","authenticated-orcid":false,"given":"Jin-Hee","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0424-1667","authenticated-orcid":false,"given":"Jongseon","family":"Choe","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1984-3545","authenticated-orcid":false,"given":"Wanjoo","family":"Chun","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sohrabi, H.R., and Weinborn, M. (2019). Cognitive impairments in Alzheimer\u2019s disease and other neurodegenerative diseases. Neurodegeneration and Alzheimer\u2019s Disease: The Role of Diabetes, Genetics, Hormones, and Lifestyle, John Wiley & Sons.","DOI":"10.1002\/9781119356752.ch9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s40120-022-00338-8","article-title":"Alzheimer\u2019s disease: Epidemiology and clinical progression","volume":"11","author":"Byrnes","year":"2022","journal-title":"Neurol. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Breijyeh, Z., and Karaman, R. (2020). Comprehensive review on Alzheimer\u2019s disease: Causes and treatment. Molecules, 25.","DOI":"10.3390\/molecules25245789"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"28","DOI":"10.37022\/jiaps.v7i1.274","article-title":"Review on Alzheimer\u2019s disease: Past, present and future","volume":"7","author":"Rayathala","year":"2022","journal-title":"J. Innov. Appl. Pharm. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1007\/s00204-019-02538-y","article-title":"Management of oxidative stress and other pathologies in Alzheimer\u2019s disease","volume":"93","author":"Simunkova","year":"2019","journal-title":"Arch. Toxicol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sehar, U., Rawat, P., Reddy, A.P., Kopel, J., and Reddy, P.H. (2022). Amyloid beta in aging and Alzheimer\u2019s disease. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms232112924"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Agarwal, U., Kapoor, G., and Tonk, R.K. (2025). A Review on the Pathophysiology of Alzheimer\u2019s Disease. Curr. Protein Pept. Sci.","DOI":"10.2174\/0113892037416859250915091846"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1016\/j.neurobiolaging.2015.07.009","article-title":"Disruption of cholinergic neurotransmission exacerbates A\u03b2-related cognitive impairment in preclinical Alzheimer\u2019s disease","volume":"36","author":"Lim","year":"2015","journal-title":"Neurobiol. Aging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2174\/1570159X13666150716165726","article-title":"Alzheimer\u2019s disease: Targeting the cholinergic system","volume":"14","author":"Talita","year":"2016","journal-title":"Curr. Neuropharmacol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Z.-R., Huang, J.-B., Yang, S.-L., and Hong, F.-F. (2022). Role of cholinergic signaling in Alzheimer\u2019s disease. Molecules, 27.","DOI":"10.3390\/molecules27061816"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.2174\/1389557517666170120153930","article-title":"Acetylcholinesterase: A primary target for drugs and insecticides","volume":"17","author":"Thapa","year":"2017","journal-title":"Mini Rev. Med. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Moss, D.E. (2020). Improving anti-neurodegenerative benefits of acetylcholinesterase inhibitors in Alzheimer\u2019s disease: Are irreversible inhibitors the future?. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21103438"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1021\/acschemneuro.8b00391","article-title":"Reconsideration of anticholinesterase therapeutic strategies against Alzheimer\u2019s disease","volume":"10","author":"Wang","year":"2018","journal-title":"ACS Chem. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11795735211029113","DOI":"10.1177\/11795735211029113","article-title":"The state of the art on acetylcholinesterase inhibitors in the treatment of Alzheimer\u2019s disease","volume":"13","author":"Vecchio","year":"2021","journal-title":"J. Cent. Nerv. Syst. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sridhar, G.R. (2021). Acetylcholinesterase inhibitors (galantamine, rivastigmine, and donepezil). NeuroPsychopharmacotherapy, Springer.","DOI":"10.1007\/978-3-319-56015-1_418-1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1093\/ajhp\/54.24.2805","article-title":"Donepezil: An anticholinesterase inhibitor for Alzheimer\u2019s disease","volume":"54","author":"Shintani","year":"1997","journal-title":"Am. J. Health-Syst. Pharm."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1111\/bph.14030","article-title":"Beyond symptomatic effects: Potential of donepezil as a neuroprotective agent and disease modifier in Alzheimer\u2019s disease","volume":"174","author":"Kim","year":"2017","journal-title":"Br. J. Pharmacol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"964","DOI":"10.2174\/1567205015666180613112040","article-title":"Strategies for Continued Successful Treatment in Patients with Alzheimer\u2019s Disease: An Overview of Switching Between Pharmacological Agents","volume":"15","author":"Blesa","year":"2018","journal-title":"Curr. Alzheimer Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/bs.seda.2021.09.012","article-title":"Side effects of drugs used in the treatment of Alzheimer\u2019s disease","volume":"Volume 43","author":"Hoffman","year":"2021","journal-title":"Side Effects of Drugs Annual"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"411","DOI":"10.2165\/00023210-200620050-00005","article-title":"Cardiovascular effects and risk of syncope related to donepezil in patients with Alzheimer\u2019s disease","volume":"20","author":"Bordier","year":"2006","journal-title":"CNS Drugs"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, S., Qian, W., Zhang, Z., Chen, Y., Hou, X., Min, B., Zhou, H., Zhu, X., Ling, J., and Yang, W. (2025). Comprehensive safety assessment of donepezil: Pharmacovigilance analysis based on the FDA adverse event reporting system. Front. Neurol., 16.","DOI":"10.3389\/fneur.2025.1655216"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10345","DOI":"10.1021\/acs.jmedchem.7b01922","article-title":"Why are the majority of active compounds in the CNS domain natural products? A critical analysis","volume":"61","author":"Bharate","year":"2018","journal-title":"J. Med. Chem."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bors, L.A., and Erd\u0151, F. (2019). Overcoming the blood\u2013brain barrier. challenges and tricks for CNS drug delivery. Sci. Pharm., 87.","DOI":"10.3390\/scipharm87010006"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"698","DOI":"10.2174\/1567201816666190828153017","article-title":"Crossing the blood-brain barrier: A review on drug delivery strategies for treatment of the central nervous system diseases","volume":"16","author":"Mansor","year":"2019","journal-title":"Curr. Drug Deliv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1093\/bib\/bbz103","article-title":"Computational\/in silico methods in drug target and lead prediction","volume":"21","author":"Agamah","year":"2020","journal-title":"Brief. Bioinform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Naithani, U., and Guleria, V. (2024). Integrative computational approaches for discovery and evaluation of lead compound for drug design. Front. Drug Discov., 4.","DOI":"10.3389\/fddsv.2024.1362456"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.csbj.2016.04.004","article-title":"Computational approaches in target identification and drug discovery","volume":"14","author":"Katsila","year":"2016","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brogi, S. (2019). Computational approaches for drug discovery. Molecules, 24.","DOI":"10.3390\/molecules24173061"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e1554","DOI":"10.1002\/wnan.1554","article-title":"Integration of target discovery, drug discovery and drug delivery: A review on computational strategies","volume":"11","author":"Duarte","year":"2019","journal-title":"Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s44371-025-00373-y","article-title":"Integrating phytochemicals and in silico methods for modern drug discovery: A comprehensive review","volume":"2","author":"Babalola","year":"2025","journal-title":"Discov. Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5625","DOI":"10.2174\/0929867325666180517094023","article-title":"A systematic review on donepezil-based derivatives as potential cholinesterase inhibitors for Alzheimer\u2019s disease","volume":"26","author":"Korabecny","year":"2019","journal-title":"Curr. Med. Chem."},{"key":"ref_32","first-page":"57","article-title":"Neurocognitive Disorders: Systematic Review of Approved (2008\u20132024) and Pipeline Phase III Medications","volume":"22","author":"Totlani","year":"2025","journal-title":"Innov. Clin. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Liu, B., Zhou, L., Zeng, W., Tian, X., Jiang, J., and Dai, D. (2025). Safety issues of donepezil combined with memantine in Alzheimer\u2019s disease population: Real-world pharmacovigilance. Expert Opin. Drug Saf., 1\u201311.","DOI":"10.1080\/14740338.2025.2467180"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL Database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_35","unstructured":"Landrum, G., Tosco, P., Kelley, B., Rodriguez, R., Cosgrove, D., Vianello, R., Gedeck, P., Jones, G., Kawashima, E., and Nealschneider, D. (2025). rdkit\/rdkit: 2025_03_1 (Q1 2025) Release, Zenodo."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Burley, S.K., Berman, H.M., Kleywegt, G.J., Markley, J.L., Nakamura, H., and Velankar, S. (2017). Protein Data Bank (PDB): The single global macromolecular structure archive. Methods in Molecular Biology, Humana Press.","DOI":"10.1007\/978-1-4939-7000-1_26"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.ijbiomac.2022.05.009","article-title":"Crystal structure of human acetylcholinesterase in complex with tacrine: Implications for drug discovery","volume":"210","author":"Dileep","year":"2022","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1093\/nar\/gkg565","article-title":"VADAR: A web server for quantitative evaluation of protein structure quality","volume":"31","author":"Willard","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1002\/jcc.10306","article-title":"Detailed analysis of grid-based molecular docking: A case study of CDOCKER\u2014A CHARMm-based MD docking algorithm","volume":"24","author":"Wu","year":"2003","journal-title":"J. Comput. Chem."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e02991","DOI":"10.1002\/slct.202502991","article-title":"Computational Identification of FDA-Approved Drugs as STAT3 Dimerization Inhibitors for Therapeutic Applications","volume":"11","author":"Yasir","year":"2026","journal-title":"ChemistrySelect"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yasir, M., Park, J., Han, E.-T., Park, W.S., Han, J.-H., Choe, J., Hassan, M., Kloczkowski, A., and Chun, W. (2025). Computational Analysis of GAT1 Mutations: Functional Consequences from Molecular Dynamics and Binding Free Energy Calculations. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms262311339"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yasir, M., Park, J., Choe, J., Han, J.-H., Han, E.-T., Park, W.S., and Chun, W. (2025). Structure-Guided Identification of JAK2 Inhibitors: From Similarity to Stability and Specificity. Future Pharmacol., 5.","DOI":"10.3390\/futurepharmacol5040066"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yasir, M., Park, J., Han, E.-T., Han, J.-H., Park, W.S., Choe, J., and Chun, W. (2025). Integration of Deep Learning with Molecular Docking and Molecular Dynamics Simulation for Novel TNF-\u03b1-Converting Enzyme Inhibitors. Future Pharmacol., 5.","DOI":"10.3390\/futurepharmacol5040055"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yasir, M., Park, J., Han, E.-T., Han, J.-H., Park, W.S., Choe, J., and Chun, W. (2025). Investigating Natural Product Inhibitors of IKK\u03b1: Insights from Integrative In Silico and Experimental Validation. Molecules, 30.","DOI":"10.3390\/molecules30092025"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3109\/10611869808997889","article-title":"Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors","volume":"6","author":"Camenisch","year":"1998","journal-title":"J. Drug Target."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1023\/A:1015040217741","article-title":"Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs","volume":"16","author":"Kelder","year":"1999","journal-title":"Pharm. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1002\/jps.2600830806","article-title":"Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the \u0394log P parameter of Seiler","volume":"83","author":"Abraham","year":"1994","journal-title":"J. Pharm. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1602\/neurorx.2.4.541","article-title":"Medicinal chemical properties of successful central nervous system drugs","volume":"2","author":"Pajouhesh","year":"2005","journal-title":"NeuroRx"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7559","DOI":"10.1021\/jm060642i","article-title":"Structure\u2212 brain exposure relationships","volume":"49","author":"Hitchcock","year":"2006","journal-title":"J. Med. Chem."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1021\/cn100008c","article-title":"Moving beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties","volume":"1","author":"Wager","year":"2010","journal-title":"ACS Chem. Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"46","author":"Lipinski","year":"2001","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s10822-011-9478-1","article-title":"Qualitative prediction of blood\u2013brain barrier permeability on a large and refined dataset","volume":"25","author":"Muehlbacher","year":"2011","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2200150","DOI":"10.1002\/adtp.202200150","article-title":"Drug Permeability: From the Blood-Brain Barrier to the Peripheral Nerve Barriers","volume":"6","author":"Sun","year":"2023","journal-title":"Adv. Ther."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1002\/jcc.21367","article-title":"CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields","volume":"31","author":"Vanommeslaeghe","year":"2010","journal-title":"J. Comput. Chem."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/nmeth.4067","article-title":"CHARMM36: An improved force field for folded and intrinsically disordered proteins","volume":"14","author":"Huang","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"641a","DOI":"10.1016\/j.bpj.2015.11.3431","article-title":"CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM\/OpenMM simulations using the CHARMM36 additive force field","volume":"110","author":"Lee","year":"2016","journal-title":"Biophys. J."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Irrgang, M.E., Davis, C., and Kasson, P.M. (2022). gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support. PLoS Comput. Biol., 18.","DOI":"10.1371\/journal.pcbi.1009835"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6281","DOI":"10.1021\/acs.jctc.1c00645","article-title":"gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS","volume":"17","author":"Valiente","year":"2021","journal-title":"J. Chem. Theory Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1021\/ct300418h","article-title":"MMPBSA. py: An efficient program for end-state free energy calculations","volume":"8","author":"Miller","year":"2012","journal-title":"J. Chem. Theory Comput."}],"container-title":["Pharmaceuticals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8247\/19\/7\/1120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T04:15:06Z","timestamp":1784780106000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8247\/19\/7\/1120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,20]]},"references-count":59,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["ph19071120"],"URL":"https:\/\/doi.org\/10.3390\/ph19071120","relation":{},"ISSN":["1424-8247"],"issn-type":[{"value":"1424-8247","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,20]]}}}