In this study, we proposed a new molecular generative model that can explore chemical spaces unreachable by previous molecular generative models and discover new molecules with druglike properties by combining deep learning and reinforcement learning based on a molecular graph representation. Specifically, the proposed method uses chemical features which learned physicochemical properties of known compounds using the Variational Graph Auto-Encoder (VGAE) and generates molecules with desirable properties through reinforce learning with Monte Carlo Tree Search (MCTS) . Evaluation of the generated molecules demonstrated that the validity and novelty of the chemical and the optimization of physicochemical properties was equivalent to or better than the previous methods. Furthermore, investigating the chemical structure diversity showed that the generated molecules are distributed in chemical space that was not well explored by the previous methods. The proposed method is expected to be useful for efficiently discovering and designing new molecules in the drug development.