The sampled molecular properties serve as conditioned inputs to our GPT-based molecular generator. By feeding the model with inputs that are statistically representative of realistic molecular configurations, we effectively guide the generation process towards chemically viable and drug-like molecules. This approach not only enhances the chemical relevance of the generated molecules but also streamlines the discovery process by focusing on candidates with higher potential for successful synthesis and development.