Our workflow adopted a deep learning-driven workflow for the de novo design of inhibitor molecules. ChemBERTa's PubChem-10M dataset includes tens of millions of drug molecule data entries, which meet various characteristics and constraints required for drug molecules. We utilized the Gemma-3B large language model architecture and pre-trained it on this dataset, enabling the pre-trained Gemma-3B to acquire the fundamental features of drug molecules. This provides a foundation for fine-tuning models for downstream inhibitor molecule design and significantly reduces the amount of data needed during fine-tuning.