For training, we utilized an extensive dataset from PubChem , which included around 77 million molecules. To ensure manageability and relevance, only those molecules with a length of fewer than 200 characters were selected, aligning with typical lengths of small to medium-sized molecules. This preselection criteria helped in maintaining a focus on computationally feasible molecular sizes during model training and subsequent generative tasks. In this study, we utilized the open-source dataset from PubChem comprising approximately 77 million molecules, previously employed in the training of the ChemBERTa model. To facilitate our analysis, we calculated six critical molecular properties for each molecule using the RDKit cheminformatics software . These properties include Molecular Weight (MolWt), Heavy Atom Count, Ring Count, Molecular LogP (MolLogP), Quantitative Estimate of Drug-likeness (QED), and synthetic accessibility score (sascore). Each of these properties was chosen for its relevance in assessing the drug-likeness and synthetic feasibility of molecules, crucial factors in drug discovery and design. The Molecular Weight provides insight into the size of the molecule, Heavy Atom Count gives the number of non-hydrogen atoms, Ring Count indicates the number of ring structures, MolLogP measures lipophilicity, QED offers a quantitative estimate of drug-likeness, and sascore assesses the ease of synthesis. The distribution of each of these molecular properties across the dataset is illustrated in Figure . This visualization helps in understanding the chemical space covered by the dataset and the typical profiles of molecules it contains. Analyzing these distributions is essential for setting appropriate conditions for molecule generation, ensuring that the generated molecules are representative of realistic, synthesizable, and potentially pharmacologically active compounds.