Molecular Optimization. Starting with an set of good molecules I (Initial set), the goal of molecular optimization is to generate a set of high-quality molecules C (Constrained set) which satisfy or optimize a set of properties P . High novelty and diversity (detailed in Section 5) are also desired for de novo generation applications. We model the molecular optimization problem as a Markov decision process (MDP), defined by the 5-tuple {S, A, p, r, ρ 0 }, where the state space S is the set of all possible molecular graphs. As an overview, our method introduces novel designs over the action space A and the transition model p (Section 4.1) by utilizing a distributional fragment vocabulary, learned by a VQ-VAE. We define the reward and initial state distribution, r and ρ 0 (Section 4.2) accordingly to implement the proposed sequential translation generation scheme. An illustration of our model is in Figure .