The task of chemical reaction predictions (CRPs) plays a pivotal role in
advancing drug discovery and material science. However, its effectiveness is
constrained by the vast and uncertain chemical reaction space and challenges in
capturing reaction selectivity, particularly due to existing methods'
limitations in exploiting the data's inherent knowledge. To address these
challenges, we introduce a data-curated self-feedback knowledge elicitation
approach. This method starts from iterative optimization of molecular
representations and facilitates the extraction of knowledge on chemical
reaction types (RTs). Then, we employ adaptive prompt learning to infuse the
prior knowledge into the large language model (LLM). As a result, we achieve
significant enhancements: a 14.2% increase in retrosynthesis prediction
accuracy, a 74.2% rise in reagent prediction accuracy, and an expansion in the
model's capability for handling multi-task chemical reactions. This research
offers a novel paradigm for knowledge elicitation in scientific research and
showcases the untapped potential of LLMs in CRPs.

为了提高化学反应预测准确性，这项研究介绍了一种数据筛选的自我反馈知识引导方法，将先前的知识融入大型语言模型，取得了显著的预测准确度提升，同时拓展了多任务化学反应的处理能力。