Another key point for molecule generation is to generate new molecular samples which possess certain properties. Early work enforces bias on the distribution of the data and trains the generative models with known desired properties to generate molecules with desired properties, while recent works mainly leverage latent space gradient-based , reinforcement learning-based , and searching-based approaches to generate molecules with desired properties. Latent space gradient-based methods are quite flexible and can work directly on both the molecules and the learned latent vectors . Reinforcement learning-based methods usually formulate controllable generation as a sequential decision-making problem and require a score-function to reward the agent.