The efficacy of the model was assessed through its ability to generate a high-quality dataset characterized by a high Quantitative Estimate of Druglikeness (QED). The methodology employed for this evaluation is detailed in the subsequent section. Upon processing 7.2 million conditions through the generator, the model successfully produced 6.5 million valid SMILES strings, achieving a generation success rate of 90.27%. Among these valid SMILES strings, 4,636,508 were unique, representing a novelty rate of 71.33%. And compared with the original training data 4.43% of the generated molecules is the same in training database, representing a novelty of generation of 95.57%.