Our TRPV4-Gemma inherits the transformer structure from Gemma and utilizes the SMILES-based tokenizer , which uses the character-level byte-pair encoding instead of byte-level to avoid Out-of-Vocabulary (OOV). Adapters are utilized in both pre-training process and fine-tuning process. We use Low-Rank Adaptation LoRA , which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. After the generation task, we construct deep-learning-based affinity prediction datasets, train from scratch using open-source models , and predict the affinity between the generated molecules and targets. According to the predicted affinity, some promising candidate molecules are selected for binding free energy calculations by computational chemistry methods to show that our model has the potential to be an efficient and useful drug discovery tool.