Single Property Manipulation. To qualitatively evaluate the performance of our method for molecule manipulation, we randomly select the successful manipulation paths from all three generative models in Fig. . The figures show that our method successfully learns interpretable and steerable directions. For example, for HierVAE in Fig. , we can find that gradually increasing LogP of a molecule may lead to the removal of the heavy atoms O and N from the structure. With respect to QED, the molecule drops double bonds, as well as heavy N and O atoms, when increasing QED for the HierVAE model. A similar trend can be observed in the MoFlow model that increasing QED drops double bonds and O atoms on the left of Fig. . Multi-Property Manipulation. When it comes to multi-property manipulation, the goal is to control multiple molecular properties of a given molecule at the same time. In Fig. (left), we show how our method manipulates multiple molecular properties. For simplicity, we remove the dupli-cate molecules and only leave the distinct molecules during the manipulation. From the figure, we can observe some correlations between LogP and QED since when we increase QED, LogP also increases accordingly.