# OpenChemIE: An Information Extraction Toolkit For Chemistry Literature

arXiv: https://arxiv.org/abs/2404.01462 · HTML: https://mlsanity.com/paper/2404.01462/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Pix2seq: A Language Modeling Framework for Object Detection](https://mlsanity.com/paper/2109.10852/) (2021)
- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation](https://mlsanity.com/paper/2205.14311/) (2022)
- [Molecular Structure Extraction From Documents Using Deep Learning](https://mlsanity.com/paper/1802.04903/) (2018)
- [ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning](https://mlsanity.com/paper/2002.09914/) (2020)
- [RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing](https://mlsanity.com/paper/2305.11845/) (2023)
- [MolMiner: You only look once for chemical structure recognition](https://mlsanity.com/paper/2205.11016/) (2022)
- [Predictive Chemistry Augmented with Text Retrieval](https://mlsanity.com/paper/2312.04881/) (2023)

## Built on by (most cited first, 17 of 17)

- [El Agente: An Autonomous Agent for Quantum Chemistry](https://mlsanity.com/paper/2505.02484/) (2025)
- [LLMatDesign: Autonomous Materials Discovery with Large Language Models](https://mlsanity.com/paper/2406.13163/) (2024)
- [From Generalist to Specialist: A Survey of Large Language Models for Chemistry](https://mlsanity.com/paper/2412.19994/) (2024)
- [MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures](https://mlsanity.com/paper/2503.16096/) (2025)
- [ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data](https://mlsanity.com/paper/2506.23520/) (2025)
- ["DIVE" into Hydrogen Storage Materials Discovery with AI Agents](https://mlsanity.com/paper/2508.13251/) (2025)
- [MolMole: Molecule Mining from Scientific Literature](https://mlsanity.com/paper/2505.03777/) (2025)
- [Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing](https://mlsanity.com/paper/2603.15011/) (2026)
- [MarkushGrapher-2: End-to-end Multimodal Recognition of Chemical Structures](https://mlsanity.com/paper/2603.28550/) (2026)
- [Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation](https://mlsanity.com/paper/2508.05427/) (2025)
- [zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature](https://mlsanity.com/paper/2508.09995/) (2025)
- [ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding](https://mlsanity.com/paper/2605.17214/) (2026)
- [VERDICT: Agreement Beats Pixel-Space Verification in Real-Document OCSR](https://mlsanity.com/paper/2608.22183/) (2026)
- [DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents](https://mlsanity.com/paper/2609.06703/) (2026)
- [ViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision](https://mlsanity.com/paper/2609.33603/) (2026)
- [Learning Transferable Reaction Mechanisms from Visual Chemical Knowledge](https://mlsanity.com/paper/2609.33608/) (2026)
- [ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning](https://mlsanity.com/paper/2610.17183/) (2026)

## Abstract (arXiv)

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of ours attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.