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- W4328049101 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." @default.
- W4328049101 created "2023-03-22" @default.
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- W4328049101 date "2023-02-01" @default.
- W4328049101 modified "2023-09-23" @default.
- W4328049101 title "Machine learning study: from the toxicity studies to tetrahydrocannabinol effects on Parkinson's disease" @default.
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- W4328049101 doi "https://doi.org/10.4155/fmc-2022-0181" @default.
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