A Self-feedback Knowledge Elicitation Approach for Chemical Reaction
  Predictions
Pengfei Liu Jun Tao Zhixiang Ren
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.