Chemical Knowledge

Chemical knowledge representation and reasoning are being significantly advanced by the application of large language models (LLMs), particularly multimodal models incorporating visual information like molecular structures. Current research focuses on developing and benchmarking LLMs for various chemical tasks, including property prediction, reaction optimization, and question answering, often employing techniques like contrastive learning and self-supervised learning to improve model performance and interpretability. These advancements hold significant promise for accelerating drug discovery, materials science, and other chemical research domains by automating complex tasks and providing more efficient and insightful analysis of chemical data.