Classification of Dopamine D2 receptor ligands using RDKit Molecular descriptors and Machine Learning Algorithms

Identifying and classifying dopamine D2 receptor agonists and antagonists is essential for the drug discovery and development. In this study, we employed machine learning algorithms, namely, XGBoost, LGBM, ExtraTree, and AdaBoost Classifier, in combination with RDKit molecular descriptors, to classify dopamine D2 receptor ligands. The dataset consisted of 195 molecules, comprising 69 dopamine agonists and 126 dopamine antagonists. The models were trained using 75% of the dataset and evaluated on the remaining 25%. The classifiers demonstrated high accuracy and F1 scores, with the AdaBoost Classifier achieving the highest accuracy of 92%. Receiver operating characteristic (ROC) analysis further confirmed the robustness of the model, as indicated by the area under the curve (AUC) values. The AUC values for the AdaBoost, Extra Tree, LGBM, and XGB classifiers were 0.92, 0.90, 0.87, and 0.89, respectively. Feature selection analysis revealed the important molecular descriptors that significantly contribute to the classification models. The ExtraTree classifier selected the highest number of descriptors (167), while the intersection of the selected descriptors among all models indicated 24 common features that crucial for classification. Classification of external compounds using the developed models revealed that sinedabet was classified as a dopamine D2 receptor antagonist, while lisuride, ropinirole, and quinpirole were classified as dopamine D2 receptor agonists.