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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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

Gilchan Park¹, Byung-Jun Yoon¹, Xihaier Luo¹

¹Computing and Data Sciences, Brookhaven National Laboratory, Upton, New York, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|May 19, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) can automate the extraction of genome-scale molecular interaction data, improving biological knowledge discovery. Larger LLMs show promise in identifying complex gene and protein interactions, though challenges remain in recognizing diverse gene groups.

Keywords:

KEGG pathway biomedical natural language processing gene regulatory relation large language model low-dose radiation protein-protein interaction question answering

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Understanding biomolecular interactions is crucial for deciphering biological systems.
Expert curation of molecular interaction data is time-consuming and labor-intensive.

Purpose of the Study:

To explore the use of large language models (LLMs) for automating genome-scale molecular interaction knowledge extraction.
To evaluate LLM performance on tasks like protein-protein interaction identification and gene regulatory relationship inference.

Main Methods:

Evaluation of various LLMs on biological tasks.
Analysis of LLM performance in identifying protein-protein interactions, radiation-influenced genes, and gene regulatory relationships.
Comparison with established molecular interaction and pathway databases.

Main Results:

Larger LLMs generally perform better in extracting complex gene and protein interactions.
LLMs show potential in identifying relevant biomolecules and predicting their interactions.
Challenges exist in recognizing functionally diverse gene groups and highly correlated regulatory relationships.

Conclusions:

LLMs offer a promising avenue for AI-driven biological knowledge discovery.
Automated extraction of molecular interaction data can significantly accelerate research.
Further development is needed to address current LLM limitations in biological data analysis.