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Understanding protein interactions and pathway knowledge is crucial forunraveling the complexities of living systems and investigating the underlyingmechanisms of biological functions and complex diseases. While existingdatabases provide curated biological data from literature and other sources,they are often incomplete and their maintenance is labor-intensive,necessitating alternative approaches. In this study, we propose to harness thecapabilities of large language models to address these issues by automaticallyextracting such knowledge from the relevant scientific literature. Toward thisgoal, in this work, we investigate the effectiveness of different largelanguage models in tasks that involve recognizing protein interactions,identifying genes associated with pathways affected by low-dose radiation, andgene regulatory relations. We thoroughly evaluate the performance of variousmodels, highlight the significant findings, and discuss both the futureopportunities and the remaining challenges associated with this approach. Thecode and data are available at: https://github.com/boxorange/BioIE-LLM

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge