Literature Mining System for Nutraceutical Biosynthesis: From AI Framework to Biological Insight

2025-12-22

AI Summary

In nutritional science research, automated extraction of “nutrient–microbial strain” associations from literature remains hampered by insufficient structural representation and low extraction accuracy. To address this, we propose a domain-adaptive large language model (LLM) prompting framework tailored for nutrient biosynthesis, integrating strain-aware query formulation, few-shot learning, and biomedical entity relation extraction. Leveraging DeepSeek-V3, we construct the first experimentally validated, high-quality structured dataset comprising 35 nutrient–strain pairs. Our method significantly improves strain identification accuracy—outperforming LLaMA2—and enables the first systematic characterization of dominant strains (e.g., *Corynebacterium glutamicum*, *Escherichia coli*) and evolutionary patterns of emerging synthetic microbial consortia. The resulting knowledge base is both interpretable and reusable, directly supporting precision fermentation and synthetic biology design of high-value nutrients.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The extraction of structured knowledge from scientific literature remains a major bottleneck in nutraceutical research, particularly when identifying microbial strains involved in compound biosynthesis. This study presents a domain-adapted system powered by large language models (LLMs) and guided by advanced prompt engineering techniques to automate the identification of nutraceutical-producing microbes from unstructured scientific text. By leveraging few-shot prompting and tailored query designs, the system demonstrates robust performance across multiple configurations, with DeepSeekV3 outperforming LLaMA2 in accuracy, especially when domain-specific strain information is included. A structured and validated dataset comprising 35 nutraceutical-strain associations was generated, spanning amino acids, fibers, phytochemicals, and vitamins. The results reveal significant microbial diversity across monoculture and co-culture systems, with dominant contributions from Corynebacterium glutamicum, Escherichia coli, and Bacillus subtilis, alongside emerging synthetic consortia. This AI-driven framework not only enhances the scalability and interpretability of literature mining but also provides actionable insights for microbial strain selection, synthetic biology design, and precision fermentation strategies in the production of high-value nutraceuticals.

Problem

• Automates identification of nutraceutical-producing microbes from text
• Extracts structured knowledge from literature for biosynthesis research
• Enhances strain selection and design for nutraceutical production

Innovation

• LLM-powered system automates microbe identification from texts
• Few-shot prompting enhances accuracy in domain-specific literature mining
• Generates validated dataset for nutraceutical-strain associations