2026-10-19T06:17:01Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/122172026-10-18T21:13:21Zopenairedrivercom_ebyu_10000col_ebyu_10001

Machine learning study: from the toxicity studies to tetrahydrocannabinol effects on Parkinson's diseaseYucel, Mehmet AliOzcelik, IbrahimAlgul, OztekinAHRdopamine receptormachine learningParkinson's diseasetetrahydrocannabinolApomorphineDopamine AgonistsDronabinolHumansMolecular Docking SimulationParkinson Diseaseabacavirantiparkinson agentarylhydrocarbon receptorbenflumetolcarbidopacrystal violetdiosmetindopaminedopamine 1 receptordopamine receptor stimulating agentendocannabinoidfebuxostatgalantamineimatinibimiquimodlansoprazoleleflunomideligandminocyclineAim: 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. © 2023 Newlands Press.Newlands Press Ltd2023Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/12217https://doi.org/10.4155/fmc-2022-01811756-8919enFuture Medicinal Chemistryinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/12217