First, the basic performance of VGAE-MCTS in generating molecules was evaluated with the Distribution-Learning Benchmarks in the GuacaMol framework. The molecules generated by VGAE-MCTS had a validity of 100%. This result is due to the fact that the chemical structure is represented as a molecular graph and the MCTS creates molecules by connecting atoms and bonds while protecting the number of valence electrons. The uniqueness and novelty scores of VGAE-MCTS were also higher. These results are thought to be due to the fact that VGAE-MCTS is able to output a wide variety of molecules because the more atoms that make up a molecule, the more molecules are candidates for expansion, and the type of atoms selected is stochastic.