Table shows that the improved value of PLogP was slightly less than GraphAF but was still sufficiently better than that of the other three methods. The success rate was approximately 1, indicating that PLogP can be optimized regardless of starting molecules. Note that the comparison of the methods here is not completely fair because they have different generation schemes. For example, GraphAF and GCPN generate molecules after fully optimizing the policy model through reinforcement learning, whereas MERMAID and the proposed method exhibit a disadvantage in that they do not train the network for a specific evaluation function.