Next, we evaluated the ability of the PoE chemical language model to control a specific molecular property using molecular docking against the DRD2 as a case study. To obtain the expert and anti-expert models for this target, we fine-tuned the 85M prior model using the DOCKSTRING dataset, which contains approximately 260,000 compounds docked against 58 medicinally relevant targets (e.g., DRD2) using AutoDock Vina. Specifically, compounds with DRD2 docking scores less than or equal to -11.1, representing the top 5% of scores found in the DOCKSTRING dataset, were labeled as DRD2+ and used to train the expert model. Conversely, compounds with DRD2 docking scores greater than -11.1 were labeled as DRD2-and used to train the anti-expert model.