However, even when focusing on prediction reliability to mitigate reward hacking, maintaining reliability in multi-objective optimization is not straightforward. First, the strategy of merging multiple ADs is undesirable except in cases where multiple prediction models are trained on the same dataset. As prediction models have unique ADs depending on their training data, each AD should be considered separately when multiple prediction models are employed. In cases where multiple ADs are handled separately, as shown in the leftmost case of Fig. , when ADs are defined for each prediction model at high reliability levels, they may not overlap if the training data for each model are distant in chemical space. In such situations, it is inherently difficult to generate molecules that fall within all ADs. As a compromise, it may be possible to overlap multiple ADs by lowering the reliability levels (center and right cases in Fig. ). However, excessively low reliability levels may produce unreliable molecules, and thus, higher reliability levels are desirable. A further complication is the difficulty in determining the appropriate reliability level before performing the molecular design (Fig. ). De novo molecular design may be successful even if no molecules have been reported in the potential overlap of the ADs (upper right of Fig. ). However, there is no guarantee that the generative model will always succeed in designing molecules within the potential overlap (middle right of Fig. ). If the reliability levels are too strict (bottom right of Fig. ), the molecular design will be difficult, but whether it is actually impossible cannot be determined until the design process is executed.