While our current models are proficient in inheriting properties from training datasets, they have yet to reach a level of innovative insight that could rival human chemists. There is a need to explore how additional knowledge can refine model parameters using more efficient techniques. Reinforcement learning from Human Feedback (RLHF) methods present an enticing avenue for exploration, which we intend to delve into in subsequent experiments. Our forthcoming work is dedicated to bridging this gap as well, focusing on creating and employing models that learn from data and integrate creativity and intuition in the molecular design process.