We examine the property of latent space learned by the generative models and have the following observations, (1) molecules with similar structures tend to cluster in the latent space, (2) interpolating two molecules x 1 and x 2 , represented by latent vectors z 1 and z 2 , can lead to a list of intermediate molecules whose structures/properties gradually change from x 1 to x 2 . As molecular structures determine molecular properties , the observations imply that molecules with similar property values of certain molecular property would cluster together and interpolating two molecules with different values of the molecular property could lead to gradual changes in molecular structures. As shown in Fig. , there may exist two groups of molecules, drug-like and drug-unlike, where each group cluster together and linear interpolating two latent vectors with one molecule from each group could lead to a direction that crosses the property separation boundary. These observations also match the analysis from the prior work . To verify our assumption, we visualize the latent space of the pre-trained MoFlow model in Fig. . The left figure shows that molecules close in the latent space are similar in structures.