In recent years, machine learning-based molecular design has made great technological advances and has achieved remarkable outcomes in drug discovery and materials science . The design objective here is to identify the chemical structure of a new molecule with a given set of desired properties. To do so, a machine learning model that forwardly predicts the physicochemical properties of any given chemical structure is first constructed and then its inverse mapping is found to determine the design of the chemical structure that exhibits the desired properties in the backward direction. The former is often referred to as quantitative structure-property relationship (QSPR) analysis , and the latter is called inverse structure-property relationship (inverse-QSPR) analysis . Usually, the inverse problem is solved using heuristic search techniques such as a genetic algorithm. A molecular generator is then used to sequentially modify a candidate molecule such that the resulting predicted properties fall into the region of the desired properties. The generative model plays an important role in this process. Traditionally, structural modifications have been performed by conducting stochastic recombination with a predefined set of molecular fragments or random atomic substitution. By utilizing fragments of existing molecules as building blocks, we can restrict the degrees of freedom in the resulting chemical structures and narrow down the search space to enhance the synthesizability of virtually created molecules. However, excessive narrowing of the search space may reduce the novelty of the structures created. In order to overcome this limitation, increasing attention has been paid, since around 2017-2018 in particular, to the development of molecule generative models that rely on advanced machine learning techniques . With a training set of compounds synthesized thus far, a generative model for molecular graphs is constructed to mimic the rule of frequently appearing chemical fragments and bonding patterns in the training molecules. Using such a model, we can freely scan the vast chemical space to identify innovative hypothetical molecules.