Finally, we evaluated whether the molecules generated by VGAE-MCTS were able to expand the chemical space from the training data. Because the ZINC dataset is registered for drug-like compounds, many molecules have a large QED, a quantitative measure of drug-likeness (approximately 92% of the molecules in the ZINC have a QED ≥ 0.5). Therefore, we can evaluate whether the generated molecules have expanded their chemical space using the chemical space of drug-like molecules in the ZINC as a reference. or better performance than existing models in the GuacaMol benchmark. We also showed that the performance of the optimization of the physicochemical properties, QED and penalized logP, is comparable or better than previous studies. In addition, to assess the diversity of chemical structures generated, we evaluated the distribution of molecules generated by VGAE-MCTS and several previous models in chemical space. The results indicate that the molecules generated by VGAE-MCTS are distributed in areas that were not well explored by the molecules generated by the previous models. Based on these results, it is expected that our proposed VGAE-MCTS will be able to propose molecules that may have been out of the scope of exploration so far, which will be useful for drug development.