We have developed a new molecular design model that combines a deep learning model, Variational Graph Auto-Encoder (VGAE), and a reinforcement learning model, Monte Carlo Tree Search (MCTS). Our developed model (called VGAE-MCTS) is divided into three parts: a part for preparation of input data, a part for training of VGAE, and a part for molecular generation using MCTS (Figure ). Details of each part are described in Materials and Methods section. The basic performance of molecular generation using VGAE-MCTS, namely validity, uniqueness, novelty, Kullback-Leibler divergence (KL divergence), and Fréchet ChemNet Distance (FCD) was evaluated with Distribution-Learning Benchmarks in the GuacaMol framework (Table ). We compared the performance of VGAE-MCTS among the previous models that are Graph MCTS and VGAE .