Decoding the Structure–Activity Relationship of the Dopamine D3 Receptor-Selective Ligands Using Machine and Deep Learning Approaches

Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia, and substance use disorders (SUDs). Evidence indicates that D3-selective ligands can effectively modulate reward pathways, offering potential in treating SUDs with reduced side effects. However, the high homology between D2 and D3 presents challenges in developing subtype-selective ligands, crucial for elucidating receptor-specific functions and developing targeted therapeutics. Here, to facilitate selective ligand discovery, we leveraged ligand-based quantitative structure-activity relationship (QSAR) modeling approaches to predict binding affinity at D2 and D3, as well as ligand selectivity for D3. We first queried training data from the ChEMBL database and performed a systematic curation process to enhance the data quality. We then developed QSAR models using eXtreme Gradient Boosting, random forest, and deep neural network (DNN) algorithms, with DNN benefiting from a novel hyperparameter optimization protocol. All models exhibited strong predictive performance, with DNN-based models slightly but consistently outperforming the tree-based models. Integrating predictions from all algorithms into a consensus metric further improved the accuracy and robustness. Notably, our selectivity models outperformed the affinity models, likely due to noise cancellation achieved by subtracting the binding affinities of the two receptors. The Shapley Additive explanations analysis revealed key pharmacophoric and physicochemical features critical for receptor affinity and selectivity, while molecular docking of representative D3-selective compounds highlighted the structural basis of D3 selectivity. These findings provide a robust framework for modeling QSARs at D2 and D3, advancing the rational design of targeted therapeutics for these receptors.