Apart from the inhibitor template molecules, we did not include any additional guiding information to ensure that the generated molecules better meet our criteria (such as higher QED, higher affinity, etc.). In future work, this will be an essential area of research. Reinforcement Learning from Human Feedback (RLHF) methods provide an exciting opportunity to incorporate more theoretical chemical knowledge into the generation of high-quality molecular content . By integrating RLHF into our future work, we can enhance the model's capability to produce molecules that not only fit within specific chemical spaces but also exhibit desirable properties, such as higher QED scores and increased affinity. This approach leverages the valuable insights and expertise of human chemists, allowing for a more informed and guided training process.