Tsuda and coworkers have combined the text-based generative model developed by Segler et al. with a Monte Carlo tree search (MCTS) algorithm to optimize molecular properties. In this approach each character in a SMILES string corresponds to a node in a tree network and each node is selected sequentially using the MCTS approach. Inspired by Virshup et al. we developed a graph-based generative model (GB-GM) that grows the molecule one atom at a time and that can be combined with a MCTS. GB-GM uses the "append atom" and "insert atom" mutations (Figure ) for atom addition to non-ring and ring atoms, respectively, but with different probabilities as described below. In addition to these two mutation-types, a new tricyclic ring-creation mutation is used: X∼Y → X1∼Z∼Y1 where "1" indicates that X and Y are bonded and "∼" indicates an arbitrary bond order.