In Step 2, the molecules are designed using a generative model to enter the overlapping region of the ADs given in Step 1 and perform multi-objective optimization. In this study, we employed ChemTSv2 , which has proven performance in various molecular designs ranging from photo-functional materials to drug design , to generate molecules using a recurrent neural network (RNN) and a Monte Carlo tree search (MCTS) . Details of the ChemTSv2, reward design, and configuration setup for execution are described in the Methods section. The properties of the designed molecules were evaluated using prediction models based on supervised learning, and the details of the models used in this study are presented in the Methods section. If the settings in Step 1 are not appropriate, the overlap of the prepared ADs may not be sufficiently wide or the optimization of the properties may fail.