论文

Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non- invasive, stable, and finely tunable nature of light-triggered methodologies. The Generative Pre-trained Transformer (GPT) has been employed for generating molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV- GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through Quantum chemical simulations. The synergy of these findings underscores the successful application of GPT technology in this critical domain.