“More recently, cannabinoid molecules have been widely studied for their potential to treat various diseases.

We used a multidisciplinary approach, combining molecular docking and machine learning tools, to identify cannabinoid-based molecules as potential acetylcholinesterase inhibitors.

We brought together molecules from the classes of cannabinoids, stilbenoids, isoflavones, and other natural products, along with their electronic structure and absorption, distribution, metabolism, excretion and tolerable toxicity (ADMET) data. A novel machine learning framework (MolSimEx, Molecular Similarity Explorer) combining K-means clustering,

Pearson correlation, and principal component analysis was developed to address the similarities of these groups. From the dataset, 30 molecules were selected based on docking scores below -11 kcal/mol. The K-means clustering yielded high classification accuracy on the dataset, correctly grouping the cannabinoid analogues. Additionally, these analogues clustered with classical acetylcholinesterase inhibitors such as huprine-X, huprine-W, and donepezil when considering ADMET and electronic descriptor data.

Radulanin J showed the highest correlation (0.41) with donepezil’s profile, suggesting the potential of cannabinoid-derived compounds as acetylcholinesterase inhibitors.”

https://pubmed.ncbi.nlm.nih.gov/41373674

“We have identified a set of 30 molecules, out of 253 derived from phytocannabinoids, flavonoids, and terpenoids, as potential new inhibitors against the hAChE (6O4W) enzyme.”

https://www.mdpi.com/1422-0067/26/23/11520

“Acetylcholinesterase inhibitors (AChEIs) are drugs that block the enzyme acetylcholinesterase, preventing the breakdown of the neurotransmitter acetylcholine, thus increasing its levels in the brain to improve nerve cell communication. They are used primarily to treat symptoms of Alzheimer’s disease”

“Provides a blueprint for developing new treatments for Alzheimer’s by targeting AChE, a key enzyme in neurotransmission.”