The COlleCtion of Open Natural ProdUcTs (CO-CONUT) database was used for model training. Salts, disconnected structures, stereochemistry, and charge annotations were removed. Molecules with canonical SMILES strings longer than 100 characters were used to train the models. A random search strategy was adopted to tune the hyper-parameters of all models, as previously explained. The models were given a five-day limit on a cloud NVIDIA A100 GPU and 1,024 strings were generated by each model. The models with the highest SMILES validity were selected for further evaluation.