ArticleChemical science2024
Fine-tuning large language models for chemical text mining.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Large Language Model Guided Discovery of Hole Transport Layer Dopants for Efficient and Stable Perovskite Photovoltaics.Small methods · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- DeepRetro discovers retrosynthetic pathways through iterative large language model reasoning.Scientific reports · 2026Article
- Advancing battery research through large language models: A review.Innovation (Cambridge (Mass.)) · 2026Review
- An evidence-weighted knowledge framework with path-aware reasoning for personalized lifestyle management in chronic disease.Frontiers in public health · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- MOF-ChemUnity: Literature-Informed Large Language Models for Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Article
- Synergizing a knowledge graph and large language model for relay catalysis pathway recommendation.National science review · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- NMRExtractor: leveraging large language models to construct an experimental NMR database from open-source scientific publications.Chemical science · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations.Journal of chemical information and modeling · 2025Article
- Large Language Models and Their Applications in Drug Discovery and Development: A Primer.Clinical and translational science · 2025Review
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions.Angewandte Chemie (International ed. in English) · 2025Article
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extracting knowledge from complex and diverse chemical texts is a pivotal task for both experimental and computational chemists. The task is still considered to be extremely challenging due to the complexity of the chemical language and scientific literature. This study explored the power of fine-tuned large language models (LLMs) on five intricate chemical text mining tasks: compound entity recognition, reaction role labelling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance spectroscopy (NMR) data extraction, and the conversion of reaction paragraphs to action sequences. The fine-tuned LLMs demonstrated impressive performance, significantly reducing the need for repetitive and extensive prompt engineering experiments. For comparison, we guided ChatGPT (GPT-3.5-turbo) and GPT-4 with prompt engineering and fine-tuned GPT-3.5-turbo as well as other open-source LLMs such as Mistral, Llama3, Llama2, T5, and BART. The results showed that the fine-tuned ChatGPT models excelled in all tasks. They achieved exact accuracy levels ranging from 69% to 95% on these tasks with minimal annotated data. They even outperformed those task-adaptive pre-training and fine-tuning models that were based on a significantly larger amount of in-domain data. Notably, fine-tuned Mistral and Llama3 show competitive abilities. Given their versatility, robustness, and low-code capability, leveraging fine-tuned LLMs as flexible and effective toolkits for automated data acquisition could revolutionize chemical knowledge extraction.
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Registered trials
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