The second approach converts the optimization task into a continuous optimization problem utilizing the latent space of generative models and employing continuous space optimization algorithms such as gradient descent. Under this category, autoencoder architectures such as variational autoencoders are often utilized to form a continuous space, most often operating on the SMILES representation of molecules . Models that attempt to perform controlled generation of molecular graphs, instead of SMILES, have also been explored , . Nevertheless, chemical validity remains a challenge as there is no guarantee that a point in the latent space corresponds to a valid molecule. However, significant advancements have been made in generative models by utilizing novel architectures as well as training modifications to improve validity and continuity of the latent space. Still, the use of continuous space optimization algorithms, such as gradient descent, assumes a smooth space with respect to the property of interest which does not naturally occur. To enforce a structured continuous latent space with respect to a specific property, joint training of generative models with property prediction models has been used , .