ChemKGL: Bridging Knowledge Graphs and Large Language Models for Chemical Multi-Step Reaction Pathway Inference

Abstract

Large language models have shown promising potential in chemistry, with prior work exploring molecular recognition, classification, and property prediction. Despite the achieved progress, LLMs are still far from satisfactory when dealing with complex chemical multi-step reaction pathway inference task due to the lack of domain knowledge and limited ability to maintain consistent multi-step reasoning. To address these challenges, we propose ChemKGL, a novel multi-step reasoning framework enhanced by knowledge graph retrieval. We reformulate the task as a knowledge graph retrieval problem to better control LLM generation, and leverage graph technologies to integrate textual descriptions, reaction conditions, and materials. Based on this reformulation, we design a novel multi-retrieval strategy to improve the performance of reaction pathway inference, which includes forward retrieval, reverse retrieval, and historical retrieval. An entropy-based pruning mechanism is further introduced to alleviate cold-start issues caused by retrieval failure. In addition, we construct a comprehensive chemical knowledge graph and corresponding dataset to support model training and analysis. Finally, we employ multiple evaluation methods to assess our proposed ChemKGL, and the experimental results demonstrate its superiority. Our code and dataset can be obtained at https://github.com/Double-Sail/ChemKGL.