The improvement cycle allows the model to adapt to new chemical spaces by incorporating structurally similar molecules into the training process. By fine-tuning the model on these augmented datasets, we enhance its ability to predict structures for novel compounds not represented in the original training data. The performance quantification utilized averaged correct sample probability and averaged Tanimoto similarity (via greedy sampling). Post-fine-tuning, multinomial sampling generated three candidates per target molecule, from which the highest Tanimoto similarity was used for assessment. The PubChem evaluation expanded testing to three molecular weight ranges: 0-250, 250-350, and 350-500 Daltons, with 100 molecules in each category. This allowed assessment of model performance on molecular weights varying from the initial training data. The same improvement cycle was applied to the PubChem dataset. Additionally, we fine-tuned the ZINC-trained MMT model on the entire PubChem training set for 18 epochs, comparing its performance to that achieved through targeted improvement cycles on smaller subsets. A second improvement iteration was conducted using the checkpoint from the smallest dataset (10 analogs) testing for the iterative improvement capabilities of the model. The fine-tuning learning rate was increased to 2e -4 for this iteration, applied across all molecular weight settings in the PubChem dataset.