The development of the GPT-like conditional molecule generator marks a significant advancement in the field of computational chemistry and drug design. The ability to generate a database of 2 million molecules with high quantitative estimation of drug-likeness (QED ¿ 0.9) validates the model's capability to effectively integrate critical physicochemical properties into the Figure : Distributions of the QED for generated molecules generation process. This study's outcomes highlight the potential of machine learning models to contribute meaningfully to the early stages of drug discovery by providing a rapid, reliable means to generate compounds with targeted properties. Future work will focus on refining the model's accuracy and expanding its application to include a broader range of chemical entities, ultimately aiming to enhance the efficiency and innovativeness of drug development.