Accurate parameter settings are critical for training deep learning models, as these choices impact the learning duration and the quality of the generated outcomes. During molecular generation, the greater the number of SMILES strings produced, the higher the probability of exploring the chemical space. Here, we opted to generate 1,000 SMILES strings per fine-tuning cycle.