To ensure that the input conditions for our GPT model reflect realistic molecular properties, we first performed a comprehensive statistical analysis of our molecular dataset. We segmented molecular properties into bins based on their Quantitative Estimate of Drug-likeness (QED) scores and calculated the mean and covariance of critical molecular descriptors within each bin. These descriptors included molecular weight, heavy atom count, ring count, partition coefficient, QED itself, and synthetic accessibility score. The binning approach facilitated a nuanced understanding of property distributions across different levels of drug-likeness.