The two main learning methods for molecular generative models are deep learning and reinforcement learning . Since deep learning-based models, which learn molecular features of known compounds , tend to generate molecules similar to the learned compounds, ability of generating structurally new compounds is fundamentally limitted . On the other hand, reinforcement learning-based models, which learn molecular features from scratch without prior learning of known compounds, is superior in generating molecules with structures distinct from known compounds . However, the generated molecules fundamentally lack drug-like properties due to the algorithm exploring chemical space distinct from the existing compounds.