In this study, we fine-tune an open-source large language model to extract structured reaction information from unstructured text from US patents (Figure ). To structure the desired outputs, we adopt the Open Reaction Database (ORD) data format, a comprehensive data schema tailored to organic reactions. The 100,000-reaction dataset we use for fine-tuning is part of a collection originally published by Lowe et al. in Chemical Markup Language (CML) format, so the fine-tuned model essentially pursues the same goal as Lowe's expert natural language processing pipeline, albeit using a different data schema. Extracted records cover information on reactants, products, conditions, and workup steps. We demonstrate that the fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for chemical messages (compounds, workups, conditions) and 92.25% for individual data fields. We also investigate its failure modes and evaluate performance on reaction role classification. We note that a preliminary version of this study was previously disclosed as part of a Perspective article on opportunities for LLMs in chemistry. as represented in the Open Reaction Database (ORD) is used to fine-tune and evaluate LLaMa-2-7B. An example of the structured ORD record is included in section 2.1. The data pipeline (top left) is detailed in section 2.2. The fine-tuning procedure is described in section 2.3. The llama with a cap was generated using Craiyon AI. 2 Methods