Chemical Language Model

Chemical language models (CLMs) leverage the power of large language models to represent and process chemical information, primarily aiming to improve molecule generation, property prediction, and chemical space exploration. Current research focuses on developing robust CLMs using transformer architectures, often pre-trained on massive datasets of molecular strings (like SMILES and SELFIES) and fine-tuned for specific tasks; techniques like instruction tuning and incorporating structural knowledge are also being explored to enhance model performance and interpretability. These advancements hold significant promise for accelerating drug discovery, materials science, and other chemical research by automating tasks and enabling more efficient exploration of chemical space.