“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 (AHR) 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.”
https://pubmed.ncbi.nlm.nih.gov/36942739/
“A machine learning model was developed to predict AHR activity of anti-Parkinson’s and US FDA-approved drugs separately. The model predicted apomorphine in anti-Parkinson’s drugs, 73 molecules in FDA-approved drugs and tetrahydrocannabinol as active.”
https://www.future-science.com/doi/10.4155/fmc-2022-0181