Although DyRAMO is a general framework for avoiding reward hacking in multiobjective optimization, there is still room for improvement, even when focusing on molecular design. First, it is necessary to investigate how to establish an appropriate definition of the reliability or confidence of a prediction model. Although this study adopted a simple and basic method of defining AD, MTS from the training data and various other definitions of AD have been proposed. For instance, in addition to distance-based methods, such as Tanimoto similarity, range-based and probability density distribution-based methods . The molecular representations for these calculations include fingerprints, property-based vector representations, and graph representations . The optimal approach for assessing prediction reliability differs for each dataset , making the optimization of reliability indicators crucial.