In this study, we used different pre-training datasets as well as a unified transfer learning dataset to train the CNSMolGen model and compared the results for the compounds generated (Table ). The results showed that the model generated the highest number of new compounds when using the ChemBridge-MPO dataset, followed by the use of the ChemBL-smiles dataset, while the lowest number of new compounds was generated when using the ChemBL-MPO dataset. This phenomenon may be related to the number and diversity of compounds in the datasets. The SAScore of the three datasets were further analyzed, and it was found that the average SA scores of the compounds when using the ChemBridge-MPO, ChemBL-MPO and ChemBL-smiles datasets were 2.77, 2.69 and 2.63, respectively, which were relatively close and with low mean values, suggesting that different training datasets have a synthetic approachability is limited. Subsequently, we compared the CNS scores of the three datasets and found that the average CNS scores corresponding to the ChemBridge-MPO, ChemBL-MPO and ChemBL-smiles datasets were 4.15, 4.19 and 4.35, respectively, which suggests that differences in the training datasets do not affect the chemical properties of compounds generated by the model. This suggests that the change in the pre-trained dataset does not have an impact on the final properties of molecule generation, but it is worth noting that the largest number of molecules were generated after transfer learning when ChemBridge-MPO was selected as the pre-trained dataset, followed by ChemBL-MPO. We analyzed this to be due to the fact that ChemBridge-MPO contains more data, while the molecules in ChemBL-MPO are more consistent with the results of transfer learning. This result suggests that the size of the original dataset will have a more direct impact on the final generated results during the transfer learning process. The selection of suitable similar datasets will also have a good impact on the generation of molecules. In order to entirely evaluate the promising application of the CNSMolGen in the field of CNS drug design, the 215 candidate molecules generated from the fine-tuned model were submitted for a comparative analysis of their binding modes and binding affinities to target protein SERT . Notably, four approved drugs that cocrystalized with SERT were used as controls. They are escitalopram-SERT (PDB ID: 5I71) , sertraline-SERT (PDB ID: 6AWO) , fluvoxamine-SERT (PDB ID: 6AWP) , and paroxetine (PDB ID: 6W2C) , respectively. Here, all of the 215 molecules were first docked into the ibogaine binding pocket of SERT by considering the pocket flexibility. And the ibogaine binding site was selected because of the relatively large volume of the pocket , which were more suitable for IFD study of molecules with different scaffolds. As a result, the redocking scores of escitalopram, sertraline, fluvoxamine, and paroxetine were -9.771 kcal/mol, -9.403 kcal/mol, -8.448 kcal/mol, and -8.467 kcal/mol, respectively (Fig. ). For all of the 215 generated molecules, using the docking score of fluvoxamine (-8.448 kcal/mol) and escitalopram (-9.771 kcal/mol) as a reference, the number of generated molecules that have potential higher binding affinities were 129 (60.00%) and 89 (41.40%), respectively. Thus, compared to the known SSRI molecules, the generated molecules (e.g., compounds 1 (-10.435 kcal/mol) and 2 (-10.453 kcal/mol)) may have comparable or even better binding affinities. The detailed binding mode of the compounds 1 and 2 in SERT were shown in Fig. and 8B. Among them, the ligand-receptor interaction of compound 1 is mainly due to the hydrogen bond interaction formed by amino acid residues Y95, A96, S336 with the ligand; compound 2 is mainly due to the hydrogen bond interaction of D98, F335 with the ligand. In addition, it was found that although the generated molecules have different structures from the approved drugs, they share similar binding conformations at the pocket (Fig. and). Despite the inherent limitations of molecular docking techniques, the results of this study suggest that the CNSMolGen opens up new possibilities for drug design targeting SERT. However, the actual biological effects of the generated molecules on SERT need to be further verified by subsequent experimental studies.