With the emergence of OpenAI's GPT model , more and more excellent open large language models have been introduced, such as GPT-4 , LLama , Gemini and so on. These causal language models are trained to predict the probability of the next word based on all preceding words. Some studies have already demonstrated the ability of language models to capture SMILES language patterns. Gupta and Segler show that the character-level RNNs can capture SMILES language patterns. Chithrananda shows that the Masked Language Model (MLM) can recover the original inputs. Adilov shows the Causal Language Models (CLM) can also learn SMILES representations and transfer to downstream tasks. CLM has attracted the attention of more researchers . Inspired by these work, we aim to generate novel TRPV4 inhibitors by more advanced transformer structure from CLM Gemma . In addition, we also use deep learning method to predict the ligand-target interaction and select some outstanding novel molecule candidates.