Benefiting from the open-source QM7b datasets provided by Quantum-Machine Project , , our photoresponsive molecular design for drug delivery has gained important datasets , . Our UV-GPT inherits the transformer structure from GPT-2 and utilizes the tokenizer from SMILES-GPT, which could facilitate downstream generation of UVresponsive molecules. By integrating predictive modeling and TDDFT calculations, we discovered that the fine-tuning UV-GPT model generate molecules with UV photoresponsive properties. The molecules generated were positively influenced by the pre-trained SMILES-GPT, as evidenced by the statistics on drug-like properties and synthesizability. These evidences indicate that our application of the combined GPT and TDDFT calculations in designing stimulus-responsive molecules holds practical value for drug delivery. However, Our present model does not completely incorporate the chemist's intuitive experience and extensive knowledge of molecular structure. This is evidenced by instances where the model occasionally produces an unstable molecule rather than a stable similar structure.