AI-driven QSAR modelling and virtual screening in the discovery of selective dopamine D2 receptor ligands.

The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved safety and efficacy. To find possible DRD2 inhibitors, we developed an integrated in silico workflow in this study that combines drug-likeness filtering, machine learning-based quantitative structure-activity relationship (ML-QSAR) modelling, and structure-based virtual screening. A standardized dataset of 1,128 DRD2 ligands with experimental inhibition constants was assembled, using pKi50 as the activity metric. Regression-based ML-QSAR models were constructed using the PubChem database and Substructure fingerprints. Random Forest techniques demonstrated the best prediction performance and robustness among these models. Crucial DRD2-binding motifs included aromatic systems, heterocycles, alkyl-aryl ethers, and halogenated groups. Strong agreement between predicted and experimental pKi50 values for FDA-approved antipsychotic medications further confirmed the validity of the model. A CNS-targeted chemical library was subjected to virtual screening, and the lead compounds were evaluated using molecular docking against the crystal structure of the dopamine D2 receptor (DRD2). VS012-7128 demonstrated strong binding affinities and formed essential interactions inside the receptor binding pocket in the molecular dynamics simulation. The study documented the effectiveness and reliability of the employed computational approach for identifying potential DRD2 ligands.