Extracting structured data from organic synthesis procedures using a fine-tuned large language model
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d4dd00091a.pdf
Description
Published version
Size
1.52 MB
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Author(s)
Ai, Qianxiang
Meng, Fanwang
Shi, Jiale
Pelkie, Brenden
Coley, Connor W
Date Issued
September 11, 2024
Journal
Digital Discovery
Publisher
Royal Society of Chemistry
Citation
Digital Discovery, 2024,3, 1822-1831
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 organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD “messages” (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1039/d4dd00091a
