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image of Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer's Disease

Abstract

Introduction

Alzheimer’s disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development.

Methods

This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins.

Results

Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with and experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets.

Discussion

The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement.

Conclusion

Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer’s disease.

© 2025 Bentham Science Publishers
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2025-10-22
2025-11-06

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References

  1. DeTure M.A. Dickson D.W. The neuropathological diagnosis of Alzheimer’s disease. Mol. Neurodegener. 2019 14 1 32 10.1186/s13024‑019‑0333‑5 31375134
    [Google Scholar]
  2. López-Antón R. Recent advances in Alzheimer’s disease research: From biomarkers to therapeutic frontiers. Biomedicines 2024 12 12 2816 10.3390/biomedicines12122816 39767722
    [Google Scholar]
  3. Zhang J. Zhang Y. Wang J. Xia Y. Zhang J. Chen L. Recent advances in Alzheimer’s disease: Mechanisms, clinical trials and new drug development strategies. Signal Transduct. Target. Ther. 2024 9 1 211 10.1038/s41392‑024‑01911‑3 39174535
    [Google Scholar]
  4. Pradeep S. Sai Chakith M.R. Sindhushree S.R. Reddy P. Sushmitha E. Purohit M.N. Suresh D. Swamy Shivananju N. Silina E. Manturova N. Stupin V. Kollur S.P. Shivamallu C. Achar R.R. Exploring shared therapeutic targets for Alzheimer’s disease and glioblastoma using network pharmacology and protein-protein interaction approach. Front Chem. 2025 13 1549186 10.3389/fchem.2025.1549186 40144222
    [Google Scholar]
  5. Jakhar R. Dangi M. Khichi A. Chhillar A.K. Relevance of molecular docking studies in drug designing. Curr. Bioinform. 2020 15 4 270 278 10.2174/1574893615666191219094216
    [Google Scholar]
  6. Sree R.R. Kalyan M. Anand N. Mani S. Gorantla V.R. Sakharkar M.K. Song B.J. Chidambaram S.B. Newer therapeutic approaches in treating Alzheimer’s disease: A comprehensive review. ACS Omega 2025 10 6 5148 5171 10.1021/acsomega.4c05527 39989768
    [Google Scholar]
  7. Congdon E.E. Sigurdsson E.M. Tau-targeting therapies for Alzheimer disease. Nat. Rev. Neurol. 2018 14 7 399 415 10.1038/s41582‑018‑0013‑z 29895964
    [Google Scholar]
  8. Passeri E. Elkhoury K. Morsink M. Broersen K. Linder M. Tamayol A. Malaplate C. Yen F.T. Arab-Tehrany E. Alzheimer’s disease: Treatment strategies and their limitations. Int. J. Mol. Sci. 2022 23 22 13954 10.3390/ijms232213954 36430432
    [Google Scholar]
  9. Tiwari S. Atluri V. Kaushik A. Yndart A. Nair M. Alzheimer’s disease: Pathogenesis, diagnostics, and therapeutics. Int. J. Nanomed. 2019 14 5541 5554 10.2147/IJN.S200490 31410002
    [Google Scholar]
  10. Jacobs H.I.L. O’Donnell A. Satizabal C.L. Lois C. Kojis D. Hanseeuw B.J. Thibault E. Sanchez J.S. Buckley R.F. Yang Q. DeCarli C. Killiany R. Sargurupremraj M. Sperling R.A. Johnson K.A. Beiser A.S. Seshadri S. Associations between brainstem volume and Alzheimer’s disease pathology in middle-aged individuals of the Framingham Heart Study. J. Alzheimers Dis. 2022 86 4 1603 1609 10.3233/JAD‑215372 35213372
    [Google Scholar]
  11. Liu X Liu Y Ji S Scilabra D Nicosia A Njavro JR Secretases related to amyloid precursor protein processing. Membranes 2021 11 12 983 10.3390/membranes11120983 34940484
    [Google Scholar]
  12. Sharma K Pradhan S Duffy LK Yeasmin S Bhattarai N Schulte MK Role of receptors in relation to plaques and tangles in Alzheimer’s disease pathology. Int. J. Mol. Sci. 2021 22 23 12987 10.3390/ijms222312987 34884789
    [Google Scholar]
  13. Fan L. Mao C. Hu X. Zhang S. Yang Z. Hu Z. Sun H. Fan Y. Dong Y. Yang J. Shi C. Xu Y. New insights into the pathogenesis of Alzheimer’s disease. Front. Neurol. 2020 10 1312 10.3389/fneur.2019.01312 31998208
    [Google Scholar]
  14. Decourt B. Noorda K. Noorda K. Shi J. Sabbagh M.N. Review of advanced drug trials focusing on the reduction of brain beta-amyloid to prevent and treat dementia. J. Exp. Pharmacol. 2022 14 331 352 10.2147/JEP.S265626 36339394
    [Google Scholar]
  15. Condello C. Merz G.E. Aoyagi A. DeGrado W.F. Prusiner S.B. Aβ and Tau prions causing Alzheimer’s disease. Methods Mol. Biol. 2023 2561 293 337 10.1007/978‑1‑0716‑2655‑9_16 36399277
    [Google Scholar]
  16. Xu X. Huang M. Zou X. Docking-based inverse virtual screening: methods, applications, and challenges. Biophys. Rep. 2018 4 1 1 16 10.1007/s41048‑017‑0045‑8 29577065
    [Google Scholar]
  17. Maia E.H.B. Assis L.C. de Oliveira T.A. da Silva A.M. Taranto A.G. Structure-based virtual screening: from classical to artificial intelligence. Front Chem. 2020 8 343 10.3389/fchem.2020.00343 32411671
    [Google Scholar]
  18. Singh S. Gupta H. Sharma P. Sahi S. Advances in Artificial Intelligence (AI)-assisted approaches in drug screening. Artificial Intell. Chem. 2024 2 1 100039 10.1016/j.aichem.2023.100039
    [Google Scholar]
  19. Gentile F Yaacoub JC Gleave J Fernandez M Ton AT Ban F Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking. Nat. Protoc. 2022 17 3 672 697 10.1038/s41596‑021‑00659‑2 35121854
    [Google Scholar]
  20. Rathke F. Hansen K. Brefeld U. Müller K.R. StructRank: A new approach for ligand-based virtual screening. J. Chem. Inf. Model. 2011 51 1 83 92 10.1021/ci100308f 21166393
    [Google Scholar]
  21. Yasuo N. Sekijima M. Improved method of structure-based virtual screening via interaction-energy-based learning. J. Chem. Inf. Model. 2019 59 3 1050 1061 10.1021/acs.jcim.8b00673 30808172
    [Google Scholar]
  22. Kimber T.B. Chen Y. Volkamer A. Deep learning in virtual screening: Recent applications and developments. Int. J. Mol. Sci. 2021 22 9 4435 10.3390/ijms22094435 33922714
    [Google Scholar]
  23. Stanzione F. Giangreco I. Cole J.C. Use of molecular docking computational tools in drug discovery. Prog. Med. Chem. 2021 60 273 343 10.1016/bs.pmch.2021.01.004 34147204
    [Google Scholar]
  24. Miserez A. Yu J. Mohammadi P. Protein-based biological materials: Molecular design and artificial production. Chem. Rev. 2023 123 5 2049 2111 10.1021/acs.chemrev.2c00621 36692900
    [Google Scholar]
  25. Vénien-Bryan C. Li Z. Vuillard L. Boutin J.A. Cryo-electron microscopy and X-ray crystallography: Complementary approaches to structural biology and drug discovery. Acta Crystallogr. F Struct. Biol. Commun. 2017 73 4 174 183 10.1107/S2053230X17003740 28368275
    [Google Scholar]
  26. Zhang S. Liu K. Liu Y. Hu X. Gu X. The role and application of bioinformatics techniques and tools in drug discovery. Front. Pharmacol. 2025 16 1547131 10.3389/fphar.2025.1547131 40017606
    [Google Scholar]
  27. sahoo RN Pattanaik S. Pattnaik G. Review on the use of molecular docking as the first line tool in drug discovery and development. Indian J. Pharm. Sci. 2022 84 5 1334
    [Google Scholar]
  28. Yuriev E. Holien J. Ramsland P.A. Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J. Mol. Recognit. 2015 28 10 581 604 10.1002/jmr.2471 25808539
    [Google Scholar]
  29. Mohapatra S. Snow D. Shea P. Gálvez-Rodríguez A. Kumar M. Padhye L.P. Mukherji S. Photodegradation of a mixture of five pharmaceuticals commonly found in wastewater: Experimental and computational analysis. Environ. Res. 2023 216 Pt 3 114659 10.1016/j.envres.2022.114659 36328221
    [Google Scholar]
  30. Dash R. Sahoo R.N. Nandi S. Swain R. Mallick S. Sustained release bioadhesive suppository formulation for systemic delivery of ornidazole: In-silico docking study. Ind. J. Pharm. Educ. Res. 2019 53 4s s580 s586 10.5530/ijper.53.4s.153
    [Google Scholar]
  31. Agu P.C. Afiukwa C.A. Orji O.U. Ezeh E.M. Ofoke I.H. Ogbu C.O. Ugwuja E.I. Aja P.M. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep. 2023 13 1 13398 10.1038/s41598‑023‑40160‑2 37592012
    [Google Scholar]
  32. Pramanik A Sahoo RN Nanda A Pradhan SK Mallick S Characterization and molecular docking of kaolin based cellulosic film for extending ophthalmic drug delivery. Indian J. Pharm. Sci. 2022 794 10.36468/pharmaceutical‑sciences.831
    [Google Scholar]
  33. Nanda A. Sahoo R.N. Pramanik A. Mohapatra R. Pradhan S.K. Thirumurugan A. Das D. Mallick S. Drug-in-mucoadhesive type film for ocular anti-inflammatory potential of amlodipine: Effect of sulphobutyl-ether-beta-cyclodextrin on permeation and molecular docking characterization. Colloids Surf. B Biointerfaces 2018 172 555 564 10.1016/j.colsurfb.2018.09.011 30218981
    [Google Scholar]
  34. Tao X. Huang Y. Wang C. Chen F. Yang L. Ling L. Che Z. Chen X. Recent developments in molecular docking technology applied in food science: A review. Int. J. Food Sci. Technol. 2020 55 1 33 45 10.1111/ijfs.14325
    [Google Scholar]
  35. Vidal-Limon A. Aguilar-Toalá J.E. Liceaga A.M. Integration of molecular docking analysis and molecular dynamics simulations for studying food proteins and bioactive peptides. J. Agric. Food Chem. 2022 70 4 934 943 10.1021/acs.jafc.1c06110 34990125
    [Google Scholar]
  36. Hajjo R. Sabbah D.A. Abusara O.H. Al Bawab A.Q. A review of the recent advances in Alzheimer’s disease research and the utilization of network biology approaches for prioritizing diagnostics and therapeutics. Diagnostics 2022 12 12 2975 10.3390/diagnostics12122975 36552984
    [Google Scholar]
  37. Arrué L. Cigna-Méndez A. Barbosa T. Borrego-Muñoz P. Struve-Villalobos S. Oviedo V. Martínez-García C. Sepúlveda-Lara A. Millán N. Márquez Montesinos J.C.E. Muñoz J. Santana P.A. Peña-Varas C. Barreto G.E. González J. Ramírez D. New drug design avenues targeting Alzheimer’s disease by pharmacoinformatics-aided tools. Pharmaceutics 2022 14 9 1914 10.3390/pharmaceutics14091914 36145662
    [Google Scholar]
  38. Meng X.Y. Zhang H.X. Mezei M. Cui M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Computeraided Drug Des. 2011 7 2 146 157 10.2174/157340911795677602 21534921
    [Google Scholar]
  39. Alrouji M. Alhumaydhi F.A. Alsayari A. Sharaf S.E. Shafi S. Anwar S. Shahwan M. Atiya A. Shamsi A. Targeting Sirtuin 1 for therapeutic potential: Drug repurposing approach integrating docking and molecular dynamics simulations. PLoS One 2023 18 12 e0293185 10.1371/journal.pone.0293185 38117829
    [Google Scholar]
  40. Burle S.S. Gupta K.R. Jibhkate Y.J. Hemke A.T. Umekar M.J. Insights into molecular docking: A comprehensive view. Int. J. Pharm. Chem. Anal. 2023 10 3 175 184 10.18231/j.ijpca.2023.030
    [Google Scholar]
  41. Pantsar T Poso A. Binding affinity via docking: Fact and fiction. Molecules 2018 23 8 1899 10.3390/molecules23081899
    [Google Scholar]
  42. Ferreira LG Dos Santos RN Oliva G Andricopulo AD Molecular docking and structure-based drug design strategies. Molecules 2015 20 7 13384 13421 10.3390/molecules200713384 26205061
    [Google Scholar]
  43. Gagliardi L. Rocchia W. SiteFerret: Beyond simple pocket identification in proteins. J. Chem. Theory Comput. 2023 19 15 5242 5259 10.1021/acs.jctc.2c01306 37470784
    [Google Scholar]
  44. Hossain M.S. Hussain M.H. multi-target drug design in Alzheimer’s Disease treatment: Emerging technologies, advantages, challenges, and limitations. Pharmacol. Res. Perspect. 2025 13 4 e70131 10.1002/prp2.70131 40531439
    [Google Scholar]
  45. Siddappaji K.K. Gopal S. Molecular mechanisms in Alzheimer’s disease and the impact of physical exercise with advancements in therapeutic approaches. AIMS Neurosci. 2021 8 3 357 389 10.3934/Neuroscience.2021020 34183987
    [Google Scholar]
  46. Andrade-Guerrero J. Santiago-Balmaseda A. Jeronimo-Aguilar P. Vargas-Rodríguez I. Cadena-Suárez A.R. Sánchez-Garibay C. Pozo-Molina G. Méndez-Catalá C.F. Cardenas-Aguayo M.C. Diaz-Cintra S. Pacheco-Herrero M. Luna-Muñoz J. Soto-Rojas L.O. Alzheimer’s disease: An updated overview of its genetics. Int. J. Mol. Sci. 2023 24 4 3754 10.3390/ijms24043754 36835161
    [Google Scholar]
  47. François M. Karpe A.V. Liu J.W. Beale D.J. Hor M. Hecker J. Faunt J. Maddison J. Johns S. Doecke J.D. Rose S. Leifert W.R. Multi-Omics, an integrated approach to identify novel blood biomarkers of Alzheimer’s disease. Metabolites 2022 12 10 949 10.3390/metabo12100949 36295851
    [Google Scholar]
  48. Kim J. Jeong M. Stiles W.R. Choi H.S. Neuroimaging modalities in Alzheimer’s Disease: Diagnosis and clinical features. Int. J. Mol. Sci. 2022 23 11 6079 10.3390/ijms23116079 35682758
    [Google Scholar]
  49. Gao W. Yang G. Liu X. Hu K. Pan J. Wang X. Zhao Y. Xu Y. Network pharmacology and experimental verification to investigate the mechanism of isoliquiritigenin for the treatment of Alzheimer’s disease. Sci. Rep. 2025 15 1 4379 10.1038/s41598‑025‑88542‑y 39910202
    [Google Scholar]
  50. Mullane K. Williams M. Alzheimer’s disease beyond amyloid: Can the repetitive failures of amyloid-targeted therapeutics inform future approaches to dementia drug discovery? Biochem. Pharmacol. 2020 177 113945 10.1016/j.bcp.2020.113945 32247851
    [Google Scholar]
  51. Cummings J. New approaches to symptomatic treatments for Alzheimer’s disease. Mol. Neurodegener. 2021 16 1 1 13 33413517
    [Google Scholar]
  52. Hampel H. Mesulam M.M. Cuello A.C. Farlow M.R. Giacobini E. Grossberg G.T. Khachaturian A.S. Vergallo A. Cavedo E. Snyder P.J. Khachaturian Z.S. The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain 2018 141 7 1917 1933 10.1093/brain/awy132 29850777
    [Google Scholar]
  53. Huang L.K. Chao S.P. Hu C.J. Clinical trials of new drugs for Alzheimer disease. J. Biomed. Sci. 2020 27 1 18 10.1186/s12929‑019‑0609‑7 31906949
    [Google Scholar]
  54. Xu M. Wong A.H.C. GABAergic inhibitory neurons as therapeutic targets for cognitive impairment in schizophrenia. Acta Pharmacol. Sin. 2018 39 5 733 753 10.1038/aps.2017.172 29565038
    [Google Scholar]
  55. Bäckström T. Turkmen S. Das R. Doverskog M. Blackburn T.P. The GABA system, a new target for medications against cognitive impairment - Associated with neuroactive steroids. J. Intern. Med. 2023 294 3 281 294 10.1111/joim.13705 37518841
    [Google Scholar]
  56. Calvo-Flores Guzmán B. Vinnakota C. Govindpani K. Waldvogel H.J. Faull R.L.M. Kwakowsky A. The GABAergic system as a therapeutic target for Alzheimer’s disease. J. Neurochem. 2018 146 6 649 669 10.1111/jnc.14345 29645219
    [Google Scholar]
  57. Stiedl O. Pappa E. Konradsson-Geuken Ã. Ã-gren S.O. The role of the serotonin receptor subtypes 5-HT1A and 5-HT7 and its interaction in emotional learning and memory. Front. Pharmacol. 2015 6 Aug 162 10.3389/fphar.2015.00162 26300776
    [Google Scholar]
  58. Bacqué-cazenave J Bharatiya R Barrière G Delbecque JP Bouguiyoud N Di Giovanni G Serotonin in animal cognition and behavior. Int. J. Mol. Sci. 2020 21 5 1649 10.3390/ijms21051649 32121267
    [Google Scholar]
  59. Grill J.D. Cummings J.L. Novel targets for Alzheimer’s disease treatment. Expert Rev. Neurother. 2010 10 5 711 10.1586/ern.10.29 20420492
    [Google Scholar]
  60. Coray R. Quednow B.B. The role of serotonin in declarative memory: A systematic review of animal and human research. Neurosci. Biobehav. Rev. 2022 139 104729 10.1016/j.neubiorev.2022.104729 35691469
    [Google Scholar]
  61. Agrawal P Singh H Srivastava HK Singh S Kishore G Raghava GPS Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics 2019 19 S13 426 10.1186/s12859‑018‑2449‑y 30717654
    [Google Scholar]
  62. Dar AM Mir S Molecular docking: Approaches, types, applications and basic challenges. J. Anal. Bioanal. Tech. 2017 8 2 1 3 10.4172/2155‑9872.1000356
    [Google Scholar]
  63. Xia S. Chen E. Zhang Y. Integrated molecular modeling and machine learning for drug design. J. Chem. Theory Comput. 2023 19 21 7478 7495 10.1021/acs.jctc.3c00814 37883810
    [Google Scholar]
  64. Torres P.H.M. Sodero A.C.R. Jofily P. Silva-Jr F.P. Key topics in molecular docking for drug design. Int. J. Mol. Sci. 2019 20 18 4574 10.3390/ijms20184574 31540192
    [Google Scholar]
  65. Sliwoski G. Kothiwale S. Meiler J. Lowe E.W. Computational methods in drug discovery. Pharmacol. Rev. 2014 66 1 334 395 10.1124/pr.112.007336 24381236
    [Google Scholar]
  66. Vieira TF Sousa SF Comparing AutoDock and Vina in ligand/decoy discrimination for virtual screening. Appl. Sci. 2019 9 21 4538 10.3390/app9214538
    [Google Scholar]
  67. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  68. Goel S. Kumar Y. Assessing Inhibitory potential of natural compounds against BACE1 in Alzheimer’s disease: A molecular docking and molecular dynamics simulation approach. Indian J. Biochem. Biophys. 2024 61 6 345 353
    [Google Scholar]
  69. Goodsell D.S. Morris G.M. Olson A.J. Automated docking of flexible ligands: Applications of autodock. J. Mol. Recognit. 1996 9 1 1 5 10.1002/(SICI)1099‑1352(199601)9:1<1::AID‑JMR241>3.0.CO;2‑6 8723313
    [Google Scholar]
  70. Forli S. Huey R. Pique M.E. Sanner M.F. Goodsell D.S. Olson A.J. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc. 2016 11 5 905 919 10.1038/nprot.2016.051 27077332
    [Google Scholar]
  71. C S. S D.K. Ragunathan V. Tiwari P. A S. P B.D. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease. J. Biomol. Struct. Dyn. 2022 40 2 585 611 10.1080/07391102.2020.1815584 32897178
    [Google Scholar]
  72. Ghersi D. Sanchez R. Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites. Proteins 2009 74 2 417 424 10.1002/prot.22154 18636505
    [Google Scholar]
  73. Sarkar A. Concilio S. Sessa L. Marrafino F. Piotto S. Advancements and novel approaches in modified AutoDock Vina algorithms for enhanced molecular docking. Results Chem. 2024 7 101319 10.1016/j.rechem.2024.101319
    [Google Scholar]
  74. Chen E.A. Zhang Y. Can deep learning blind docking methods be used to predict allosteric compounds? J. Chem. Inf. Model. 2025 65 7 3737 3748 10.1021/acs.jcim.5c00331 40167386
    [Google Scholar]
  75. Koukos P.I. Faro I. van Noort C.W. Bonvin A.M.J.J. A membrane protein complex docking benchmark. J. Mol. Biol. 2018 430 24 5246 5256 10.1016/j.jmb.2018.11.005 30414967
    [Google Scholar]
  76. Kurkcuoglu Z. Koukos P.I. Citro N. Trellet M.E. Rodrigues J.P.G.L.M. Moreira I.S. Roel-Touris J. Melquiond A.S.J. Geng C. Schaarschmidt J. Xue L.C. Vangone A. Bonvin A.M.J.J. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2. J. Comput. Aided Mol. Des. 2018 32 1 175 185 10.1007/s10822‑017‑0049‑y 28831657
    [Google Scholar]
  77. Weng G. Wang E. Wang Z. Liu H. Zhu F. Li D. Hou T. HawkDock: A web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 2019 47 W1 W322 W330 10.1093/nar/gkz397 31106357
    [Google Scholar]
  78. Dominguez C. Boelens R. Bonvin A.M.J.J. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 2003 125 7 1731 1737 10.1021/ja026939x 12580598
    [Google Scholar]
  79. de Vries S.J. van Dijk A.D.J. Krzeminski M. van Dijk M. Thureau A. Hsu V. Wassenaar T. Bonvin A.M.J.J. HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets. Proteins 2007 69 4 726 733 10.1002/prot.21723 17803234
    [Google Scholar]
  80. Trellet M. Melquiond A.S.J. Bonvin A.M.J.J. Information-driven modeling of protein-peptide complexes. Methods Mol. Biol. 2015 1268 221 239 10.1007/978‑1‑4939‑2285‑7_10 25555727
    [Google Scholar]
  81. Koukos P.I. Réau M. Bonvin A.M.J.J. Shape-restrained modeling of protein–small-molecule complexes with high ambiguity driven DOCKing. J. Chem. Inf. Model. 2021 61 9 4807 4818 10.1021/acs.jcim.1c00796 34436890
    [Google Scholar]
  82. Elkins M.R. Sergeyev I.V. Hong M. Determining cholesterol binding to membrane proteins by cholesterol 13 C labeling in yeast and dynamic nuclear polarization NMR. J. Am. Chem. Soc. 2018 140 45 15437 15449 10.1021/jacs.8b09658 30338997
    [Google Scholar]
  83. Ibrahim I.M. Abdelmalek D.H. Elshahat M.E. Elfiky A.A. COVID-19 spike-host cell receptor GRP78 binding site prediction. J. Infect. 2020 80 5 554 562 10.1016/j.jinf.2020.02.026 32169481
    [Google Scholar]
  84. Grosdidier A Zoete V Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011 39 Web Server issue W270 W277 10.1093/nar/gkr366 21624888
    [Google Scholar]
  85. Knight I.S. Mailhot O. Tang K.G. Irwin J.J. DockOpt: A tool for automatic optimization of docking models. J. Chem. Inf. Model. 2024 64 3 1004 1016 10.1021/acs.jcim.3c01406 38206771
    [Google Scholar]
  86. Gao Y. Wang H. Zhou J. Yang Y. An easy-to-use three-dimensional protein-structure-prediction online platform “DPL3D” based on deep learning algorithms. Curr. Res. Struct. Biol. 2025 9 100163 10.1016/j.crstbi.2024.100163 39867105
    [Google Scholar]
  87. Kufareva I. Abagyan R. Methods of protein structure comparison. Methods Mol. Biol. 2011 857 231 257 10.1007/978‑1‑61779‑588‑6_10 22323224
    [Google Scholar]
  88. Kozakov D. Hall D.R. Xia B. Porter K.A. Padhorny D. Yueh C. Beglov D. Vajda S. The ClusPro web server for protein–protein docking. Nat. Protoc. 2017 12 2 255 278 10.1038/nprot.2016.169 28079879
    [Google Scholar]
  89. Das D.R. Kumar D. Kumar P. Dash B.P. Molecular docking and its application in search of antisickling agent from Carica papaya. J. Appl. Biol. Biotechnol. 2020 8 1 105 116 10.7324/JABB.2020.80117
    [Google Scholar]
  90. Abudiyab N.A. Alanazi A.T. Visualization techniques in healthcare applications: A narrative review. Cureus 2022 14 11 e31355 10.7759/cureus.31355 36514654
    [Google Scholar]
  91. Morris G.M. Green L.G. Radić Z. Taylor P. Sharpless K.B. Olson A.J. Grynszpan F. Automated docking with protein flexibility in the design of femtomolar “click chemistry” inhibitors of acetylcholinesterase. J. Chem. Inf. Model. 2013 53 4 898 906 10.1021/ci300545a 23451944
    [Google Scholar]
  92. Huang SY Zou X Advances and challenges in protein-ligand docking. Int. J. Mol. Sci. 2010 11 8 3016 3034 10.3390/ijms11083016 21152288
    [Google Scholar]
  93. Blanes-Mira C Fernández-Aguado P de Andrés-López J Fernández-Carvajal A Ferrer-Montiel A Fernández-Ballester G. Comprehensive survey of consensus docking for high-throughput virtual screening. Molecules 2022 28 1 175 10.3390/molecules28010175 36615367
    [Google Scholar]
  94. Vakser I.A. Protein-protein docking: From interaction to interactome. Biophys. J. 2014 107 8 1785 1793 10.1016/j.bpj.2014.08.033 25418159
    [Google Scholar]
  95. Pagadala N.S. Syed K. Tuszynski J. Software for molecular docking: A review. Biophys. Rev. 2017 9 2 91 102 10.1007/s12551‑016‑0247‑1 28510083
    [Google Scholar]
  96. Ha E.J. Lwin C.T. Durrant J.D. LigGrep: A tool for filtering docked poses to improve virtual-screening hit rates. J. Cheminform. 2020 12 1 69 10.1186/s13321‑020‑00471‑2 33292486
    [Google Scholar]
  97. Bitencourt-Ferreira G. de Azevedo W.F. Molegro virtual docker for docking. Methods Mol. Biol. 2019 2053 149 167 10.1007/978‑1‑4939‑9752‑7_10 31452104
    [Google Scholar]
  98. Iqbal D. Alsaweed M. Jamal Q.M.S. Asad M.R. Rizvi S.M.D. Rizvi M.R. Albadrani H.M. Hamed M. Jahan S. Alyenbaawi H. Pharmacophore-based screening, molecular docking, and dynamic simulation of fungal metabolites as inhibitors of multi-targets in neurodegenerative disorders. Biomolecules 2023 13 11 1613 10.3390/biom13111613 38002295
    [Google Scholar]
  99. Salo-Ahen OMH Alanko I Bhadane R Alexandre AM Honorato RV Hossain S Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 2020 9 1 71 10.3390/pr9010071
    [Google Scholar]
  100. Rao S.N. Head M.S. Kulkarni A. LaLonde J.M. Validation studies of the site-directed docking program LibDock. J. Chem. Inf. Model. 2007 47 6 2159 2171 10.1021/ci6004299 17985863
    [Google Scholar]
  101. Venkatachalam C.M. Jiang X. Oldfield T. Waldman M. LigandFit: A novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graph. Model. 2003 21 4 289 307 10.1016/S1093‑3263(02)00164‑X 12479928
    [Google Scholar]
  102. Li H. Komori A. Li M. Chen X. Yang A.W.H. Sun X. Liu Y. Hung A. Zhao X. Zhou L. Multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis of the synergistic effects between natural compounds baicalein and cubebin for the inhibition of the main protease of SARS-CoV-2. J. Mol. Liq. 2023 374 121253 10.1016/j.molliq.2023.121253 36694691
    [Google Scholar]
  103. Goddard T.D. Huang C.C. Ferrin T.E. Software extensions to UCSF chimera for interactive visualization of large molecular assemblies. Structure 2005 13 3 473 482 10.1016/j.str.2005.01.006 15766548
    [Google Scholar]
  104. Salmaso V. Moro S. Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Front. Pharmacol. 2018 9 AUG 923 10.3389/fphar.2018.00923 30186166
    [Google Scholar]
  105. Badar MS Shamsi S Ahmed J Alam MA Molecular dynamics simulations: Concept, methods, and applications. Transdisciplinarity Cham Springer 2022 5 131 151 10.1007/978‑3‑030‑94651‑7_7
    [Google Scholar]
  106. Butt S.S. Badshah Y. Shabbir M. Rafiq M. Molecular docking using Chimera and Autodock Vina software for nonbioinformaticians. JMIR Bioinf. Biot. 2020 1 1 e14232 10.2196/14232
    [Google Scholar]
  107. Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. Tunyasuvunakool K. Bates R. Žídek A. Potapenko A. Bridgland A. Meyer C. Kohl S.A.A. Ballard A.J. Cowie A. Romera-Paredes B. Nikolov S. Jain R. Adler J. Back T. Petersen S. Reiman D. Clancy E. Zielinski M. Steinegger M. Pacholska M. Berghammer T. Bodenstein S. Silver D. Vinyals O. Senior A.W. Kavukcuoglu K. Kohli P. Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596 7873 583 589 10.1038/s41586‑021‑03819‑2 34265844
    [Google Scholar]
  108. Meng Y. Zhang Z. Zhou C. Tang X. Hu X. Tian G. Yang J. Yao Y. Protein structure prediction via deep learning: An in-depth review. Front. Pharmacol. 2025 16 1498662 10.3389/fphar.2025.1498662 40248099
    [Google Scholar]
  109. Pinzi L Rastelli G. Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci. 2019 20 18 4331 10.3390/ijms20184331 31487867
    [Google Scholar]
  110. Aghajani J. Farnia P. Farnia P. Ghanavi J. Velayati A.A. Molecular dynamic simulations and molecular docking as a potential way for designed new inhibitor drug without resistance. Tanaffos 2022 21 1 1 14 36258912
    [Google Scholar]
  111. Hughes J.P. Rees S. Kalindjian S.B. Philpott K.L. Principles of early drug discovery. Br. J. Pharmacol. 2011 162 6 1239 1249 10.1111/j.1476‑5381.2010.01127.x 21091654
    [Google Scholar]
  112. Dnyandev K.M. Babasaheb G.V. Chandrashekhar K.V. Chandrakant M.A. Vasant O.K. A review on molecular docking. Int. Res. J. Pure Appl. Chem. 2021 22 3 60 68 10.9734/irjpac/2021/v22i330396
    [Google Scholar]
  113. dos Santos R.N. Ferreira L.G. Andricopulo A.D. Practices in molecular docking and structure-based virtual screening. Methods Mol. Biol. 2018 1762 31 50 10.1007/978‑1‑4939‑7756‑7_3 29594766
    [Google Scholar]
  114. Li Y. Liu Z. Li J. Han L. Liu J. Zhao Z. Wang R. Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. J. Chem. Inf. Model. 2014 54 6 1700 1716 10.1021/ci500080q 24716849
    [Google Scholar]
  115. Ashtawy H.M. Mahapatra N.R. Task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment. J. Chem. Inf. Model. 2018 58 1 119 133 10.1021/acs.jcim.7b00309 29190087
    [Google Scholar]
  116. Meli R. Morris G.M. Biggin P.C. Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: A review. Front. Bioinform. 2022 2 885983 10.3389/fbinf.2022.885983 36187180
    [Google Scholar]
  117. Guedes I.A. Pereira F.S.S. Dardenne L.E. Empirical scoring functions for structure-based virtual screening: Applications, critical aspects, and challenges. Front. Pharmacol. 2018 9 SEP 1089 10.3389/fphar.2018.01089 30319422
    [Google Scholar]
  118. Li J. Fu A. Zhang L. An overview of scoring functions used for protein–ligand interactions in molecular docking. Interdiscip. Sci. 2019 11 2 320 328 10.1007/s12539‑019‑00327‑w 30877639
    [Google Scholar]
  119. Amin K.M. Rahman A.D.E. Allam A.H. El-Zoheiry H.H. Design and synthesis of novel coumarin derivatives as potential acetylcholinesterase inhibitors for Alzheimer’s disease. Bioorg. Chem. 2021 110 104792 10.1016/j.bioorg.2021.104792 33799178
    [Google Scholar]
  120. Karunakaran K.B. Thiyagaraj A. Santhakumar K. Novel insights on acetylcholinesterase inhibition by Convolvulus pluricaulis, scopolamine and their combination in zebrafish. Nat. Prod. Bioprospect. 2022 12 1 6 10.1007/s13659‑022‑00332‑5 35212831
    [Google Scholar]
  121. Peitzika S.C. Pontiki E. A Review on recent approaches on molecular docking studies of novel compounds targeting acetylcholinesterase in Alzheimer disease. Molecules 2023 28 3 1084 10.3390/molecules28031084 36770750
    [Google Scholar]
  122. Correa-Basurto J. Alcántara I.V. Espinoza-Fonseca L.M. Trujillo-Ferrara J.G. p–Aminobenzoic acid derivatives as acetylcholinesterase inhibitors. Eur. J. Med. Chem. 2005 40 7 732 735 10.1016/j.ejmech.2005.03.011 15935907
    [Google Scholar]
  123. Dong H. Yuede C.M. Coughlan C.A. Murphy K.M. Csernansky J.G. Effects of donepezil on amyloid-β and synapse density in the Tg2576 mouse model of Alzheimer’s disease. Brain Res. 2009 1303 169 178 10.1016/j.brainres.2009.09.097 19799879
    [Google Scholar]
  124. Shahrivar-Gargari M. Hamzeh-Mivehroud M. Hemmati S. Shahbazi Mojarrad J. Notash B. Tüylü Küçükkılınç T. Ayazgök B. Dastmalchi S. Design, synthesis, and biological evaluation of novel indanone-based hybrids as multifunctional cholinesterase inhibitors for Alzheimer’s disease. J. Mol. Struct. 2021 1229 129787 10.1016/j.molstruc.2020.129787
    [Google Scholar]
  125. Saeedi M. Safavi M. Karimpour-Razkenari E. Mahdavi M. Edraki N. Moghadam F.H. Khanavi M. Akbarzadeh T. Synthesis of novel chromenones linked to 1,2,3-triazole ring system: Investigation of biological activities against Alzheimer’s disease. Bioorg. Chem. 2017 70 86 93 10.1016/j.bioorg.2016.11.011 27914694
    [Google Scholar]
  126. Xie X Yu T Li X Zhang N Foster LJ Peng C Recent advances in targeting the “undruggable” proteins: From drug discovery to clinical trials. Signal Transduct. Target. Ther. 2023 8 1 335 10.1038/s41392‑023‑01589‑z 37669923
    [Google Scholar]
  127. Blanco E. Izotova N. Booth C. Thrasher A.J. Immune reconstitution after gene therapy approaches in patients with X-linked severe combined immunodeficiency disease. Front. Immunol. 2020 11 608653 10.3389/fimmu.2020.608653 33329605
    [Google Scholar]
  128. Li Y Zhang N Peng X Ma W Qin Y Yao X Network pharmacology analysis and clinical verification of Jishe Qushi capsules in rheumatoid arthritis treatment. Medicine 2023 102 34 e34883 10.1097/MD.0000000000034883
    [Google Scholar]
  129. Wu X. He C. Liu C. Xu X. Chen C. Yang H. Shi H. Fei Y. Sun Y. Zhou S. Fang B. Mechanisms of JinHong Formula on treating sepsis explored by randomized controlled trial combined with network pharmacology. J. Ethnopharmacol. 2023 305 116040 10.1016/j.jep.2022.116040 36539071
    [Google Scholar]
  130. Xia M. Ai N. Pang J. Preliminary exploration of clinical efficacy and pharmacological mechanism of modified danggui-shaoyao san in the treatment of depression in patients with chronic kidney disease. Drug Des. Devel. Ther. 2022 16 3975 3989 10.2147/DDDT.S387677 36415742
    [Google Scholar]
  131. De Ravin SS Liu S Sweeney CL Brault J Whiting-Theobald N Ma M Lentivector cryptic splicing mediates increase in CD34+ clones expressing truncated HMGA2 in human X-linked severe combined immunodeficiency. Nat. Commun. 2022 13 1 3710 10.1038/s41467‑022‑31344‑x 35764638
    [Google Scholar]
  132. Cui Y. Mi J. Feng Y. Li L. Wang Y. Hu J. Wang H. Huangqi Sijunzi decoction for treating cancer-related fatigue in breast cancer patients: A randomized trial and network pharmacology study. Nan Fang Yi Ke Da Xue Xue Bao 2022 42 5 649 657 35673907
    [Google Scholar]
  133. Feng Y. Zhu B. Liu Y. Liu Y. Zhou G. Yang L. Liu L. Ren J. Hou Y. Yu H. Meng P. Jiang Y. Wang X. Yindan Jiedu granules exhibit anti-inflammatory effect in patients with novel Coronavirus disease (COVID-19) by suppressing the NF-κB signaling pathway. Phytomedicine 2022 95 153784 10.1016/j.phymed.2021.153784
    [Google Scholar]
  134. Di Pierro F. Derosa G. Maffioli P. Bertuccioli A. Togni S. Riva A. Allegrini P. Khan A. Khan S. Khan B.A. Altaf N. Zahid M. Ujjan I.D. Nigar R. Khushk M.I. Phulpoto M. Lail A. Devrajani B.R. Ahmed S. Possible therapeutic effects of adjuvant quercetin supplementation against early-stage covid-19 infection: A prospective, randomized, controlled, and open-label study. Int. J. Gen. Med. 2021 14 2359 2366 10.2147/IJGM.S318720 34135619
    [Google Scholar]
  135. Xia L. Shi Y. Su J. Friedemann T. Tao Z. Lu Y. Ling Y. Lv Y. Zhao R. Geng Z. Cui X. Lu H. Schröder S. Shufeng Jiedu, a promising herbal therapy for moderate COVID-19: Antiviral and anti-inflammatory properties, pathways of bioactive compounds, and a clinical real-world pragmatic study. Phytomedicine 2021 85 153390 10.1016/j.phymed.2020.153390 33158717
    [Google Scholar]
  136. Poudel P. Park S. Recent advances in the treatment of alzheimer’s disease using nanoparticle-based drug delivery systems. Pharmaceutics 2022 14 4 835 10.3390/pharmaceutics14040835 35456671
    [Google Scholar]
  137. O’Brien R.J. Wong P.C. Amyloid precursor protein processing and Alzheimer’s disease. Annu. Rev. Neurosci. 2011 34 1 185 204 10.1146/annurev‑neuro‑061010‑113613 21456963
    [Google Scholar]
  138. Miller V.M. Gouvion C.M. Davidson B.L. Paulson H.L. Targeting Alzheimer’s disease genes with RNA interference: an efficient strategy for silencing mutant alleles. Nucleic Acids Res. 2004 32 2 661 668 10.1093/nar/gkh208 14754988
    [Google Scholar]
  139. McConlogue L. Buttini M. Anderson J.P. Brigham E.F. Chen K.S. Freedman S.B. Games D. Johnson-Wood K. Lee M. Zeller M. Liu W. Motter R. Sinha S. Partial reduction of BACE1 has dramatic effects on Alzheimer plaque and synaptic pathology in APP Transgenic Mice. J. Biol. Chem. 2007 282 36 26326 26334 10.1074/jbc.M611687200 17616527
    [Google Scholar]
  140. Ciftci I Sever B Demirci H Alghamdi MA From molecules to medicines: The role of ai-driven drug discovery against Alzheimer's disease and other neurological disorders. Pharmaceuticals 2025 18 7 1041 10.3390/ph18071041
    [Google Scholar]
  141. Sarkar C. Das B. Rawat V.S. Wahlang J.B. Nongpiur A. Tiewsoh I. Lyngdoh N.M. Das D. Bidarolli M. Sony H.T. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int. J. Mol. Sci. 2023 24 3 2026 10.3390/ijms24032026 36768346
    [Google Scholar]
  142. Teleanu R.I. Preda M.D. Niculescu A.G. Vladâcenco O. Radu C.I. Grumezescu A.M. Teleanu D.M. Current strategies to enhance delivery of drugs across the blood–brain barrier. Pharmaceutics 2022 14 5 987 10.3390/pharmaceutics14050987 35631573
    [Google Scholar]
  143. Van Cauwenberghe C. Van Broeckhoven C. Sleegers K. The genetic landscape of Alzheimer disease: Clinical implications and perspectives. Genet. Med. 2016 18 5 421 430 10.1038/gim.2015.117 26312828
    [Google Scholar]
/content/journals/car/10.2174/0115672050386924250930184405
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Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer's Disease
Bentham Science Publishers ; https://doi.org/10.2174/0115672050386924250930184405
/content/journals/car/10.2174/0115672050386924250930184405
/content/journals/car/10.2174/0115672050386924250930184405
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  • Article Type:     Review Article
Keywords: Alzheimer's disease ; drug discovery ; tau phosphorylation ; neurodegeneration ; computational methods ; therapeutic agents ; amyloid-beta ; molecular docking

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