Enzyme Co-Scientist: Harnessing Large Language Models for Enzyme Kinetic Data Extraction from Literature

Jiang, J.; Hu, J.; Xie, S.; Guo, M.; Dong, Y.; Fu, S.; Jiang, X.; Yue, Z.; Shi, J.; Zhang, X.; Song, M.; Chen, G.; Lu, H.; Wu, X.; Guo, P.; Han, D.; Sun, Z.; Qiu, J.

doi:10.1101/2025.03.03.641178

bioRxiv · 10.1101/2025.03.03.641178

Enzyme Co-Scientist: Harnessing Large Language Models for Enzyme Kinetic Data Extraction from Literature

Jiang, J.·Hu, J.·Xie, S.·Guo, M.·Dong, Y.·Fu, S.·Jiang, X.·Yue, Z.·Shi, J.·Zhang, X.·Song, M.·Chen, G.·Lu, H.·Wu, X.·Guo, P.·Han, D.·Sun, Z.·Qiu, J.

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Abstract

The extraction of molecular annotations from scientific literature is critical for advancing data-driven research. However, traditional methods, which primarily rely on human curation, are labor-intensive and error-prone. Here, we present an LLM-based agentic workflow that enables automatic and efficient data extraction from literature with high accuracy. As a demonstration, our workflow successfully delivers a dataset containing over 91,000 enzyme kinetics entries from around 3,500 papers. It achieves an average F1 score above 0.9 on expert-annotated subsets of protein enzymes and can be extended to the ribozyme domain in fewer than 3 days at less than $90. This method opens up new avenues for accelerating the pace of scientific research.

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Jiang, J., Hu, J., Xie, S., Guo, M., Dong, Y., Fu, S., Jiang, X., Yue, Z., Shi, J., Zhang, X., Song, M., Chen, G., Lu, H., Wu, X., Guo, P., Han, D., Sun, Z., Qiu, J.. 2025-03-11. Enzyme Co-Scientist: Harnessing Large Language Models for Enzyme Kinetic Data Extraction from Literature. https://doi.org/10.1101/2025.03.03.641178

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