Automated Large-Scale Extraction of Nanotoxicity Data from the Literature Using Multimodal Large Language Models

Manual curation of nanotoxicity data remains a major bottleneck in the scalable construction of large datasets because key physicochemical and toxicological variables are often distributed across text, figures, tables, captions, legends, and Materials and Methods sections. Here, we developed a multimodal large language model (LLM) workflow for automated extraction and structured curation of nanotoxicity data from research articles. Four multimodal LLMs were benchmarked on curated set of 17 oxide nanotoxicity articles. GPT-5.4 provided the best balance between extraction accuracy and efficiency, achieving a mean extraction F1 score of 96.1% and an assay results F1 score of 86.5% on the main benchmark. The optimized workflow was then applied to 821 articles, generating 16,786 structured records and substantially reducing the manual recovery of figure-derived quantitative data. Automated machine learning (AutoML) modeling showed that the physicochemical (PChem) score-filtered subset achieved higher overall predictive performance, whereas record-count balancing among the 4 most represented materials reduced performance. Models trained only on records for ZnO, TiO₂, Fe₃O₄, or SiO₂ improved prediction for the corresponding material compared with multi-material models, but applicability domain analysis showed that these gains occurred within narrower descriptor-space coverage. These findings demonstrate that multimodal extraction can move nanotoxicity curation beyond text-only workflows while supporting data-aware model selection across broad-coverage, quality-filtered, focused multi-material, and single-material prediction strategies.