A CLM was trained to generate de novo SMILES strings of drug-like molecules by training on 189,238 unique molecules extracted from the ChEMBL database . AHC was used to fine-tune the pre-trained CLM generating 12,800 de novo molecules with the goal to minimize the GlideSP docking score against each of seven selected A 2A receptor crystal structures, as well as optimising secondary objectives to encourage more favourable drug-like properties. These secondary objectives included synthesisability , predicted logP, hydrogen bond donor count, and the maximum number of consecutive rotatable bonds present, thus presenting a more realistic and challenging multi-objective optimisation problem.