To date, studies haven't shown a clear path for applying GPT technology to drug delivery molecules and validating its efficacy. Here, we opt for a transformer structure with adapter layers, specifically the GPT-2 model. Pretrained on the PubChem dataset, this GPT-2 model aims to generate molecules with high druglikeness and easy synthesis. A SMILES-Tokenizer is developed by Sanjar Adilov, based on the atomic dictionaries and linked characters. The linked characters of a molecular SMILES give out the information about the chemical bond and molecular configuration. The SMILES-Tokenizer is also deal with Multilayer Perceptron (MLP) and attention mechanism networks. Our goal is to adapt a pre-trained GPT for generating effective photoresponsive molecules using adapter layers. Once this workflow is proved to work, we can integrate various pre-trained language models through Hugging Face. This workflow to generate stimulusresponsive molecules via pre-training language model could be found in