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.
Deep Dive into Literature Mining System for Nutraceutical Biosynthesis: From AI Framework to Biological Insight.
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 glutami
Nutraceuticals are bioactive compounds derived from food sources that provide medical or health benefits beyond basic nutrition. It includes functional foods, dietary supplements, and fortified products aimed at disease prevention and health promotion 1 .
The global nutraceuticals market was valued at approximately USD 712.97 billion in 2023 and is projected to grow at a CAGR of 8.4% through 2030, driven by increasing consumer interest in preventive health, clean-label ingredients, and personalised nutrition 2 . The United Kingdom is witnessing rapid expansion in this sector. According to GlobeNewswire, the UK nutraceutical market is expected to reach £8.36 billion by 2029, growing at a CAGR of 6.9% between 2024 and 2029. This growth is driven by strong consumer demand for functional foods and beverages that support immune health, gut health, and mental well-being. Popular trends include plant-based formulations, fermented products, bioactives such as probiotics and omega-3s, as well as innovations in precision fermentation for producing high-value compounds, including amino acids and peptides.
However, challenges persist in terms of regulatory alignment, scientific substantiation of health claims, and consumer awareness. Strict frameworks require rigorous evidence for nutrition and health claims, often limiting product approvals despite growing market demand 2,3 The integration of Artificial Intelligence (AI), particularly Large Language Models (LLMs), has transformed scientific literature mining by enabling scalable and context-aware information extraction. Traditional tools such as PubTator 4 , SciSpacy 5 , and MetaMap 6 ohered basic capabilities to perform such tasks. On the other hand, transformer-based LLMs like BioBERT 7 , SciBERT 8 , and PubMedBERT 9 have shown improved semantic understanding and domain adaptation by being pretrained or fine-tuned on biomedical corpora.
Prompt engineering has become an important technique in maximising the utility of LLMs 10,11 . State-of-the-art prompt engineering involves crafting input prompts that guide model behaviour to perform specific tasks, ranging from question answering and summarisation to domain-specific information extraction. Techniques have evolved beyond simple instruction-based prompts to include advanced formulations such as chain-of-thought prompting 12 , which encourages the model to reason step-by-step, and few-shot prompting 11 , where examples are embedded within the prompt to establish task patterns. As LLMs are deployed in increasingly complex settings, prompt engineering plays a critical role in aligning model capabilities with user intent and domain-specific requirements.
In summary, the use of LLMs provides a powerful foundation for extracting scientific knowledge in complex domains, such as the production of nutraceuticals. By leveraging prompt engineering, such systems can fill gaps in structured scientific understanding, enabling automated extraction.
A central challenge in extracting insights from scientific literature lies in the unstructured nature of the information. In domains such as nutraceutical production, important data, including microbial species, are often dispersed across text, tables, figures, and supplementary materials. The multi-modal nature of scientific outputs further compounds this fragmentation: essential experimental details may appear only in figure captions or complex tables, making them dihicult to detect and interpret using traditional text-focused tools.
Beyond literature, static data sources, including both open-access repositories and curated databases, form the foundation for AI-driven discovery in the nutraceutical domain. Platforms such as PubMed 13 , Europe PMC 14 , and arXiv host vast corpora of scientific publications, while structured databases like FooDB 15 , DSLD 16 , and NCBI Taxonomy 17 oher entries for known compounds and strains.
It is therefore beneficial to have a systematic pipeline that extracts and harmonises multi-modal content, such as text parsing, before feeding it into LLMs guided by domainspecific prompt engineering. Such an approach is crucial for developing integrated, highquality knowledge papers and datasets that can inform robust models for nutraceutical research.
Current approaches to extracting structured knowledge from scientific literature in the nutraceutical domains face several key limitations. One major gap is the lack of domainspecific tools that can accommodate the complex linguistic structures and scientific context unique to microbial fermentation and nutraceutical research. Although LLMs oher strong capabilities, they often fall short when applied without domain-adapted prompt engineering. Furthermore, the growing prevalence of multi-modal scientific data, such as textual descriptions and tables, adds another layer of complexity, limiting LLMs’ ehectiveness in generating comprehensive, structured knowledge from diverse data sources.
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