biostudies-literatureUnknown29Ha EWith the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical concerns. As a result, the qualitative and quantitative importance of data is now widely recognized. However, collecting large, high-quality data is both time-consuming and labor-intensive. Artificial intelligence (AI)-based data extraction techniques hold significant potential for extracting and organizing information from unstructured text. However, the use of large language models (LLMs) and prompt engineering for nanotoxicity data extraction has not been widely studied. In this study, we deveComputational and structural biotechnology journal138-148https://www.ebi.ac.uk/biostudies/studies/S-EPMC12008667biostudies-literatureAI-based nanotoxicity data extraction and prediction of nanotoxicity.PMC12008667Ha SMKim HYHa EYoon THGerelkhuu ZfalseAI-based nanotoxicity data extraction and prediction of nanotoxicity.With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical concerns. As a result, the qualitative and quantitative importance of data is now widely recognized. However, collecting large, high-quality data is both time-consuming and labor-intensive. Artificial intelligence (AI)-based data extraction techniques hold significant potential for extracting and organizing information from unstructured text. However, the use of large language models (LLMs) and prompt engineering for nanotoxicity data extraction has not been widely studied. In this study, we deve2025-01-01T00:00:00Z20252025-07-01T03:04:48.951Z2025-07-01T03:04:48.951ZS-EPMC120086674025545810.1016/j.csbj.2025.03.052