Policy gradient algorithms are broadly used in reinforcement learning problems with continuous action spaces . A policy, π ψ : S → P(A) where P(A) is the set of probability measure on A, guides the actions of the agent in the MDP whose goal is to maximize the cumulative total reward from start state s 0 . Policies can be either stochastic (π ψ (s) = pθ(a|s)), where actions are probabilistically conditioned on environmental states, or deterministic (µ ψ (s) = a), in which actions are predetermined. In this work we focus on the latter, specifically, we utilize a simplified version of deep deterministic policy gradient introduced by Lillicrap et al. where the reward is calculated by a neural network (R ψ ) that is trained to score molecular graphs with input from external chemoinformatics tools. We direct the reader to the work of De Cao and Kipf , specifically section 2.3, for the precise formulation of the reinforcement learning problem.