In Silico Screening and Identification of Phytoconstituents for Alzheimer's Disease


Authors : Abhishek Kumar; Dr. Kumud Madan; Ankita Tiwari

Volume/Issue : Volume 10 - 2025, Issue 6 - June


Google Scholar : https://tinyurl.com/3u8t28d3

DOI : https://doi.org/10.38124/ijisrt/25jun652

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Cognitive decline and neuronal degradation are hallmarks of Alzheimer's disease (AD), a progressive neurodegenerative illness. There are currently few therapy options for the buildup of tau tangles and amyloid-beta plaques, which are important pathological hallmarks. Through antioxidant, anti-inflammatory, and neuroprotective processes, phytoconstituent; bioactive substances derived from plants—have demonstrated promising role in slowing the course of AD. The current research work is about Identification of natural components useful in Alzheimer’s disease. Using in silico techniques. Swiss Dock was used for molecular docking investigations. The various steps included analysis of the binding affinities of specific phytoconstituent with important AD targets, such as tau protein, beta-secretase (BACE1), amyloid-beta, and acetylcholinesterase (Ache), using Auto Dock and Glide software. According to the findings, natural substances like Quercetin, Resveratrol, and Curcumin showed potent binding interactions with AD-related targets, indicating natural inhibitory properties. Docking scores were used to evaluate binding affinities, and interaction analysis showed strong hydrophobic and hydrogen bonding interactions. Further, in vitro and in vivo validation is needed. The study emphasizes how computational screening can speed up the process of finding new drugs for neurodegenerative illnesses.

References :

  1. Anand, R., Gill, K. D., & Mahdi, A. A. (2014). Therapeutics of Alzheimer's disease: Past, present and future. Neuropharmacology, 76, 27–50. (Anand et al., 2014)
  2. Ayaz, M., et al. (2019). Flavonoids as potential neuroprotectants and their therapeutic propensity in aging-associated neurological disorders. Frontiers in Aging Neuroscience, 11, 155. (Ayaz et al., 2019)
  3. Bukhari, S. N. A., et al. (2015). In silico molecular docking studies of natural products against Alzheimer's disease. Current Computer-Aided Drug Design, 11(3), 248–258. (Anand et al., 2014) (Bukhari et al., 2015)
  4. Daina, A., et al. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. (Daina et al., 2017)
  5. Fadaka, A. O., et al. (2020). Computational modelling and in silico screening of phytochemicals from African medicinal plants against targets in Alzheimer's disease. Scientific African, 10, e00656. (Anand et al., 2014) (Fadaka et al., 2020)
  6. Fang, J., et al. (2020). In silico identification of plant-based natural compounds as potential inhibitors of β-secretase. Journal of Biomolecular Structure and Dynamics, 38(18), 5307–5314. (Fang et al., 2020)
  7. Goyal, D., et al. (2017). Recent advancements and molecular dynamics in Alzheimer’s drug discovery. Current Neuropharmacology, 15(6), 724–734. (Goyal et al., 2017)
  8. Gupta, S. C., et al. (2013). Therapeutic roles of curcumin: Lessons learned from clinical trials. AAPS Journal, 15(1), 195–218. (Gupta et al., 2013)
  9. Howes, M. J. R., et al. (2003). Plants with traditional uses and activities relevant to the management of Alzheimer’s disease. Phytotherapy Research, 17(1), 1–18. (Howes et al., 2003)
  10. Iqbal, M., et al. (2020). In silico identification of novel phytochemicals as acetylcholinesterase inhibitors. Journal of Biomolecular Structure and Dynamics, 38(13), 3614–3624. (Fadaka et al., 2020) (Iqbal et al., 2020)
  11. Irfan, M., et al. (2022). In silico exploration of natural inhibitors for Alzheimer’s disease targeting acetylcholinesterase. Computers in Biology and Medicine, 141, 105135. (Irfan et al., 2022)
  12. Jha, N. K., et al. (2019). Neuroprotective role of phytochemicals against Alzheimer's disease. Current Neuropharmacology, 17(6), 526–548. (Anand et al., 2014) (Fadaka et al., 2020) (Jha et al., 2019)
  13. Khan, M. F., et al. (2020). Molecular docking and pharmacological property analysis of phytochemicals from Clitoria ternatea. Frontiers in Neuroscience, 14, 894. (Bukhari et al., 2015) (Fadaka et al., 2020) (Khan et al., 2020)
  14. Kim, H. Y., et al. (2016). Intracellular Aβ in Alzheimer’s disease. Biochemical and Biophysical Research Communications, 458(4), 733–739. (Kim et al., 2016)
  15. Kumar, A., & Singh, A. (2015). A review on Alzheimer’s disease pathophysiology and its management. Pharmacological Reports, 67(2), 195–203. (Kumar & Singh, 2015)
  16. Lee, C. Y. (2021). Antioxidant and anti-amyloidogenic effects of natural compounds. Current Alzheimer Research, 18(2), 107–120. (Lee, 2021)
  17. Li, X., et al. (2020). Phytochemicals as potential therapeutic agents for Alzheimer’s disease. Current Topics in Medicinal Chemistry, 20(2), 160–174. (Fadaka et al., 2020) (Li et al., 2020)
  18. Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technologies, 1(4), 337–341. (Goyal et al., 2017) (Lipinski, 2004)
  19. Liu, Z., et al. (2018). In silico screening of natural inhibitors targeting tau aggregation. Frontiers in Aging Neuroscience, 10, 210. (Irfan et al., 2022) (Liu et al., 2018)
  20. Ma, Q., & Xu, Y. (2019). Phytochemicals as potential therapeutic agents for Alzheimer’s. Pharmacological Research, 147, 104363. (Fadaka et al., 2020) (Li et al., 2020)
  21. Mani, V., & Venkatesan, D. (2019). In silico screening of bioactive compounds. Bioinformation, 15(4), 277–282. (Mani & Venkatesan, 2019)
  22. Mathew, B., & Paul, S. (2014). Docking studies of curcumin analogs. Chemical Biology & Drug Design, 83(6), 760–765. (Gupta et al., 2013)
  23. Orhan, I. E. (2012). Nature: A substantial source of auspicious drugs. Current Neuropharmacology, 10(4), 346–355.
  24. Pathania, A. S., et al. (2020). Phytochemical compound library database for Alzheimer’s. Scientific Data, 7, 331.
  25. Roy, A., & Jana, N. R. (2017). Alzheimer’s disease and autophagy. Current Alzheimer Research, 14(6), 635–646.
  26. Russo, P., et al. (2013). Multitarget drugs of plants origin acting on Alzheimer’s. Current Medicinal Chemistry, 20(13), 1686–1693.
  27. Saini, V., et al. (2020). Computational insights into flavonoids as cholinesterase inhibitors. Computational Biology and Chemistry, 89, 107376. (Ayaz et al., 2019)
  28. Salomone, S., et al. (2012). New pharmacological strategies for treatment. British Journal of Clinical Pharmacology, 73(4), 504–517.
  29. Singh, S. K., et al. (2020). Overview of Alzheimer’s disease and therapeutic aspects. Research Journal of Pharmacy and Technology, 13(3), 1433–1440.
  30. Ullah, R., & Khan, M. W. (2018). Role of Ginkgo biloba in cognitive functions of Alzheimer’s patients. Journal of the Chinese Medical Association, 81(3), 233–238.

Cognitive decline and neuronal degradation are hallmarks of Alzheimer's disease (AD), a progressive neurodegenerative illness. There are currently few therapy options for the buildup of tau tangles and amyloid-beta plaques, which are important pathological hallmarks. Through antioxidant, anti-inflammatory, and neuroprotective processes, phytoconstituent; bioactive substances derived from plants—have demonstrated promising role in slowing the course of AD. The current research work is about Identification of natural components useful in Alzheimer’s disease. Using in silico techniques. Swiss Dock was used for molecular docking investigations. The various steps included analysis of the binding affinities of specific phytoconstituent with important AD targets, such as tau protein, beta-secretase (BACE1), amyloid-beta, and acetylcholinesterase (Ache), using Auto Dock and Glide software. According to the findings, natural substances like Quercetin, Resveratrol, and Curcumin showed potent binding interactions with AD-related targets, indicating natural inhibitory properties. Docking scores were used to evaluate binding affinities, and interaction analysis showed strong hydrophobic and hydrogen bonding interactions. Further, in vitro and in vivo validation is needed. The study emphasizes how computational screening can speed up the process of finding new drugs for neurodegenerative illnesses.

CALL FOR PAPERS


Paper Submission Last Date
30 - June - 2025

Paper Review Notification
In 2-3 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe