Supervised Machine learning and Molecular docking modeling to Identify Potential Anti-Parkinson’s Agents
Published in Journal of Molecular Graphics and Modelling, 2025
This study used CHEMBL data to build and evaluate machine learning models with different descriptors, identifying the XGBoost model with RDkit features as the best for predicting adenosine A2A receptor inhibitors, supported by molecular docking, to aid in discovering new Anti-Parkinson’s agents.
Recommended citation: A.Ghaleb, A.Aouidate, M. Aarjane, H.Anane. (2025). "Supervised Machine learning and Molecular docking modeling to Identify Potential Anti-Parkinson’s Agents." Journal of Molecular Graphics and Modelling. 1(2).
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