However, data-driven molecular design using prediction models faces the risk of reward hacking . This phenomenon, well-known in reinforcement learning and game AI, occurs when optimization deviates unexpectedly from the intended goals . It arises when a reward function, used to guide optimization, produces unintended outputs due to inputs that deviate significantly from expected scenarios. A similar issue can arise in molecular design guided by a prediction model, leading to the design of molecules that diverge from the model's training data, resulting in reward hacking . Consequently, the optimization process deviates from its intended direction. In such cases, the seemingly favorable predicted value of the designed molecule may be inaccurate. In fact, in drug design, there have been cases where unstable or complex molecules, distinct from existing drugs, have been designed due to reward hacking .