As a molecule generator, we employed ChemTSv2 , which has been applied in materials and drug design and has been experimentally validated to have the ability to design desired molecules. ChemTSv2 combines two main components: molecular design with an RNN and chemical space exploration via MCTS. In MCTS, each node represents an atom in the molecule's Simplified Molecular Input Line Entry System (SMILES) string, exploring molecules by extending nodes to build a search tree. The exploration process using MCTS involves four steps: selection, expansion, simulation, and backpropagation. In the selection step, a leaf node is selected based on the upper confidence bound (UCB) score. The UCB score balances exploration and exploitation by adjusting the parameter C. For instance, a high C value (e.g., 1.0) emphasizes exploitation, focusing on unvisited nodes, whereas a low value (e.g., 0.1) encourages the exploration of promising nodes. In the expansion step, the selected node is expanded based on the RNN. In the simulation step, a molecule is generated by completing the SMILES part with the RNN, and is evaluated using a reward function. Finally, backpropagation updates the UCB score of the evaluated node based on the reward function. ChemTSv2 iterates these four steps to design molecules that maximize the value of the reward function. The RNN model was trained using 224,153 molecules from the ChEMBL database. These molecules were represented as randomized SMILES to explore a wide range of chemical space.