Molecular design using generative models has recently gained significant attention as a promising technology . This approach has been applied to diverse fields, such as drug discovery and the development of functional materials . Such a design can be formulated as an inverse problem , i.e., designing molecules with desired properties. The key is to evaluate the designed molecules (calculate the reward function) using methods like molecular simulation or data-driven predictive modeling, to guide the design process. While simulation-based evaluation, grounded in physicochemical principles, is generally accurate, it can be computationally expensive. Therefore, data-driven evaluation, based on supervised learning prediction models, is frequently used . In addition, practical molecular design often requires the simultaneous optimization of multiple properties. This can be formulated as a multi-objective optimization problem , and molecular designs based on generative models and data-driven evaluations have been reported using this approach .