  Understanding protein interactions and pathway knowledge is crucial for
unraveling the complexities of living systems and investigating the underlying
mechanisms of biological functions and complex diseases. While existing
databases 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 the
capabilities of large language models to address these issues by automatically
extracting such knowledge from the relevant scientific literature. Toward this
goal, in this work, we investigate the effectiveness of different large
language models in tasks that involve recognizing protein interactions,
identifying genes associated with pathways affected by low-dose radiation, and
gene regulatory relations. We thoroughly evaluate the performance of various
models, highlight the significant findings, and discuss both the future
opportunities and the remaining challenges associated with this approach. The
code and data are available at: https://github.com/boxorange/BioIE-LLM
