At the 20th training epoch, the models with different parameter settings were able to generate more than 70% of novel, unique and valid SMILES strings. This result indicates that by pre-trained on a large set of molecules, the model has effectively mastered the SMILES syntax and is able to reliably generate molecules with potential drug properties (Fig. ). Adjusting the size of the hidden layer had a significant effect on training speed and model performance. For example, when the hidden layer size was set to 64, the ability to generate new molecules was relatively weak despite the shortest training time. In contrast, when the hidden layer size was set to 128 or 256, the model showed very similar and positive results, with around 90% of the molecules generated being valid and completely new. At the same time, increasing the number of LSTM layers resulted in the model requiring more training cycles to converge (Fig. ). Considering the training effect and the required computational resources, we found that the model with two LSTM layers showed an excellent ability to generate molecules at the 20th training epoch, successfully synthesizing more than 90% of the novel molecules.