Parameter selection: data augmentation, dropout and temperature Data augmentation, the practice of artificially increasing the volume of training data to improve model performance, was implemented by altering the starting position of the marker, allowing the model to learn from any point within the SMILES encoding. Comparative analysis showed that quintupling the data augmentation in the last five training cycles yielded the best effectiveness and novelty in the generated compounds (Fig. ).