Cholinesterase and Neurodegenerative Diseases · Journal article

Identification of Potential Natural Acetylcholinesterase Inhibitors Through Molecular Docking for Alzheimer's Disease Therapy

The American Journal of Science and Medical Research · July 17, 2026

Hypothesis-Generating Why this matters to you Expert discussion

Raises a question worth testing. It does not answer one.

AI Summary

This computational study used molecular docking to predict AChE binding affinities for eight natural phytochemicals, identifying 6,7-Dimethoxy-4-phenylcoumarin as the lead candidate. The work is purely in silico with no empirical validation, and the authors explicitly state that further in vitro and in vivo studies are required to confirm pharmacological efficacy and safety.

What was studied

Computational molecular docking study. Intervention: Molecular docking simulations to evaluate inhibitory potential of eight phytochemicals against AChE; analysis of binding affinity and interaction patterns within enzyme active site. Compared with: Standard AChE inhibitor Donepezil. n = 8.

Key findings

6,7-Dimethoxy-4-phenylcoumarin exhibited the highest binding affinity with binding energy of −8.9 kcal/mol The compound forms hydrogen bond interaction with Tyr72 and hydrophobic interactions with Tyr341, Trp286, and Leu76 Binding energy comparable to standard inhibitor Donepezil with similar interaction pattern at key active-site residues

Safety

Binding energy alone does not predict pharmacological activity or bioavailability

Who this may help

The source did not state who this applies to in practice.

Plain-language summary

This is an in silico molecular docking study with no experimental validation; it raises mechanistic questions about natural compounds as AChE inhibitors but does not answer them with empirical data.

Study design

As stated by the source record.

MethodsComputational molecular docking study

Sample size8

InterventionMolecular docking simulations to evaluate inhibitory potential of eight phytochemicals against AChE; analysis of binding affinity and interaction patterns within enzyme active site

ComparatorStandard AChE inhibitor Donepezil

Reported figures

Quoted from the source exactly as published.

Binding energy (lead compound)−8.9 kcal/mol

Binding affinity range (other compounds)−7.3 to −8.9 kcal/mol

Number of phytochemicals screened8

Caveats

No in vitro enzyme inhibition assays or kinetic data (IC50, Km, Vmax) reported
No cell-based or animal model data; therapeutic efficacy entirely unvalidated
Binding energy alone does not predict pharmacological activity or bioavailability
No information on selection criteria for the eight phytochemicals or justification of docking methodology

Evidence assessment

Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.

Study design

limited

In silico docking simulations only; no experimental or clinical validation performed.

Directness

limited

Binding affinity prediction is indirect; no enzyme inhibition assays or cellular outcomes measured.

Consistency

insufficient

No comparison of results across multiple methods or validation cohorts reported.

Risk of bias

insufficient

Computational methods and selection criteria for eight phytochemicals not detailed.

Sample adequacy

limited

Eight phytochemicals screened; no justification for sample size or selection rationale.

Source abstract

ABSTRACT Alzheimer's disease is a progressive neurodegenerative disorder characterised by cognitive impairment and memory loss, primarily associated with the degeneration of cholinergic neurons. Inhibition of Acetylcholinesterase (AChE) is considered an effective therapeutic strategy to enhance cholinergic neurotransmission and alleviate the symptoms of the disease. In the present study, molecular docking simulations were performed to evaluate the inhibitory potential of eight selected phytochemicals against AChE. The docking analysis was conducted to investigate the binding affinity and interaction patterns of these compounds within the active site of the enzyme. Among the screened phytochemicals, 6,7-Dimethoxy-4-phenylcoumarin exhibited the highest binding affinity with a binding energy of −8.9 kcal/mol, indicating the formation of a stable enzyme–ligand complex. Detailed interaction analysis revealed that the compound forms a hydrogen bond interaction with Tyr72, along with significant hydrophobic interactions involving Tyr341, Trp286, and Leu76 within the active gorge of AChE. These residues play an important role in ligand stabilisation at the peripheral anionic site and within the aromatic gorge of the enzyme. The docking results were further compared with the standard AChE inhibitor Donepezil, which demonstrated a similar binding energy and interaction pattern with key active-site residues, including Tyr72, Trp286, Tyr341, Tyr337, and Val294. In addition to the top-ranked compound, the remaining seven phytochemicals also exhibited favourable binding affinities ranging from −7.3 to −8.9 kcal/mol, indicating significant interaction with the active site of AChE and suggesting their potential inhibitory activity. These findings highlight 6,7-Dimethoxy-4-phenylcoumarin and the other screened phytochemicals as promising natural inhibitors of AChE, which may serve as potential lead compounds for the development of alternative therapeutic agents for the management of Alzheimer's disease. However, further in vitro and in vivo studies are required to validate their pharmacological efficacy and safety. References [1] Ahmad, S., et al. (2021). Natural products as acetylcholinesterase inhibitors. Journal of Enzyme Inhibition and Medicinal Chemistry, 36(1), 1–27. [2] Bartus, R. T., Dean, R. L., Beer, B., & Lippa, A. S. (1982). The cholinergic hypothesis of geriatric memory dysfunction. Science, 217(4558), 408–414. [3] Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H.,.. & Bourne, P. E. (2000). The protein data bank. Nucleic acids research, 28(1), 235-242. [4] Birks, J. (2006). Cholinesterase inhibitors for Alzheimer’s disease. Cochrane Database of Systematic Reviews, CD005593. [5] Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., & Tang, Y. (2012). ADMET prediction. Journal of Chemical Information and Modeling, 52(11), 3099–3105. [6] Cheung, J., et al. (2012). Structures of human acetylcholinesterase. Journal of Medicinal Chemistry, 55(22), 10282–10286. [7] Cheung, J., Rudolph, M. J., Burshteyn, F., Cassidy, M. S., Gary, E. N., Love, J.,.. & Height, J. J. (2012). Structures of human acetylcholinesterase in complex with pharmacologically important ligands. Journal of medicinal chemistry, 55(22), 10282-10286. [8] Colovic, M. B., Krstic, D. Z., Lazarevic-Pasti, T. D., Bondzic, A. M., & Vasic, V. M. (2013). Acetylcholinesterase inhibitors: pharmacology and toxicology. Current Neuropharmacology, 11(3), 315–335. [9] Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME. Scientific Reports, 7, 42717. [10] Dey, A., Bhattacharya, R., Mukherjee, A., & Pandey, D. K. (2017). Natural products against Alzheimer’s disease. Phytotherapy Research, 31(12), 1906–1925. [11] Ferreira, L. G., dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015). Molecular docking and structure-based drug design strategies. Molecules, 20(7), 13384–13421. [12] Francis, P. T., Palmer, A. M., Snape, M., & Wilcock, G. K. (1999). The cholinergic hypothesis of Alzheimer’s disease. Journal of Neurology, Neurosurgery & Psychiatry, 66(2), 137–147. [13] Goodsell, D. S., Morris, G. M., & Olson, A. J. (1996). Automated docking of flexible ligands: applications of AutoDock. Journal of molecular recognition, 9(1), 1-5. [14] Gujjeti, R. P., Namthabad, S., & Mamidala, E. (2014). HIV-1 reverse transcriptase inhibitory activity of Aerva lanata plant extracts. BMC Infectious Diseases, 14(Suppl 3), P12. [15] Gurrapu, S., & Mamidala, E. (2017). In Vitro HIV-1 reverse transcriptase inhibition of andrographolide isolated from Andrographis paniculata. European Journal of Biomedical, 4(12), 516-522. [16] Janakiramulu, P., & Mamidala, E. (2025). Molecular Docking and dynamic simulation analysis of flavonoid derivatives as COX-2 inhibitors. In Silico Pharmacology, 13(2), 59. [17] Janakiramulu, P., & Mamidala, E. (2025). Molecular Docking and dynamic simulation analysis of flavonoid derivatives as COX-2 inhibitors. In Silico Pharmacology, 13(2), 59. [18] Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening. Nature Reviews Drug Discovery, 3(11), 935–949. [19] Kryger, G., Silman, I., & Sussman, J. L. (1999). Structure of acetylcholinesterase complexed with E2020 (Aricept®): implications for the design of new anti-Alzheimer drugs. Structure, 7(3), 297-307. [20] Kumar Thatipamula, R., Narsimha, S., Battula, K., Chary, V. R., Mamidala, E., & Reddy, N. V. (2017). Synthesis, anticancer and antibacterial evaluation of novel (isopropylidene) uridine-[1, 2, 3] triazole hybrids. Journal of Saudi Chemical Society, 21(7), 795-802. [21] Kumar, M. P., Mamidala, E., Al-Ghanim, K., Al-Misned, F., Ahmed, Z., & Mahboob, S. (2020). Effects of D-Limonene on aldose reductase and protein glycation in diabetic rats. Journal of King Saud University-Science, 32(3), 1953-1958. [22] Lionta, E., Spyrou, G., Vassilatis, D. K., & Cournia, Z. (2014). Structure-based virtual screening for drug disc

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Cholinesterase and Neurodegenerative DiseasesKundhellabudda Sayanna Mallesh, Dhudapaka Narender, Pasula Janakiramulu