Extracting structured data from organic synthesis procedures using a fine-tuned large language model.

ABSTRACT: The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for org

SUBMITTER: Ai Q 

PROVIDER: S-EPMC11322921 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature