In this section we compare the properties of the molecules generated by the pre-trained and finetuned models, as well as the properties of the molecules in the pre-trained and fine-tuned datasets. We randomly select 100 molecules from each molecule source to analyse (if the number of molecules from a source is less than 100, all molecules are used) and compare the properties of these molecules separately. In Fig. , the molecules in the ChemBridge-MPO dataset have the highest average MPO scores, while the molecules generated by the pre-trained model have the next highest MPO scores. This indicates that CNSMolGen as a molecule generation model has a strong learning capability to effectively master the rules of molecular SMILES representation and accurately capture the features of CNS molecules. In addition, the average MPO scores of the model-generated molecules decreased after transfer learning, which may be related to the lower average MPO scores of the molecules in the transfer learning dataset. Nevertheless, the MPO scores of the molecules generated after transfer learning were still high and met the MPO criteria for CNS drugs, indicating that they are potential CNS drug candidates. It is important to note that of all the parameters affecting CNS scores, the molecules generated by deep learning had lower scores for LogP and LogD, suggesting future improvement of molecule generation models.