doi: 10.4155/fmc-2022-0181. Epub 2023 Mar 21.
- PMID: 36942739
- DOI: 10.4155/fmc-2022-0181
Abstract
Aim: Investigating molecules having toxicity and chemical similarity to find hit molecules.
Methods: The machine learning (ML) model was developed to predict the arylhydrocarbon receptor activity of anti-Parkinson’s and US FDA-approved drugs. The ML algorithm was a support vector machine, and the dataset was Tox21.
Results: The ML model predicted apomorphine in anti-Parkinson’s drugs and 73 molecules in FDA-approved drugs as active. The authors were curious if there is any molecule like apomorphine in these 73 molecules. A fingerprint similarity analysis of these molecules was conducted and found tetrahydrocannabinol (THC). Molecular docking studies of THC for dopamine receptor 1 (affinity = -8.2 kcal/mol) were performed.
Conclusion: THC may affect dopamine receptors directly and could be useful for Parkinson’s disease.
Keywords: AHR, Parkinson’s disease, dopamine receptor, machine learning, tetrahydrocannabinol
Plain language summary
Arylhydrocarbon receptor has tissue-specific roles in xenobiotic metabolism, the immune system, inflammation and cancer. Studies showed that carbidopa and dopamine are agonists of arylhydrocarbon receptor. Parkinson’s disease is a neurodegenerative disease and depends on the dopamine system’s dysregulation. There is a strong relationship between the dopamine system and cannabinoids. In this study, the possibility of the agonist effect of tetrahydrocannabinol on dopamine receptors was investigated by a machine learning method.
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