# RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing

arXiv: https://arxiv.org/abs/2305.11845 · HTML: https://mlsanity.com/paper/2305.11845/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [Pix2seq: A Language Modeling Framework for Object Detection](https://mlsanity.com/paper/2109.10852/) (2021)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [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)

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

- [ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area](https://mlsanity.com/paper/2408.07246/) (2024)
- [OpenChemIE: An Information Extraction Toolkit For Chemistry Literature](https://mlsanity.com/paper/2404.01462/) (2024)
- [From Generalist to Specialist: A Survey of Large Language Models for Chemistry](https://mlsanity.com/paper/2412.19994/) (2024)
- [Predictive Chemistry Augmented with Text Retrieval](https://mlsanity.com/paper/2312.04881/) (2023)
- [Evaluating Large Language Models on Multimodal Chemistry Olympiad Exams](https://mlsanity.com/paper/2512.14989/) (2025)
- [textttPatentAgent: Intelligent Agent for Automated Pharmaceutical Patent Analysis](https://mlsanity.com/paper/2410.21312/) (2024)
- [Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model](https://mlsanity.com/paper/2503.08156/) (2025)
- [RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning](https://mlsanity.com/paper/2511.02384/) (2025)
- [SMiCRM: A Benchmark Dataset of Mechanistic Molecular Images](https://mlsanity.com/paper/2407.18338/) (2024)
- [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)
- [Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation](https://mlsanity.com/paper/2508.05427/) (2025)
- [MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition](https://mlsanity.com/paper/2608.03525/) (2026)
- [EnzChemRED, a rich enzyme chemistry relation extraction dataset](https://mlsanity.com/paper/2404.14209/) (2024)
- [Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature](https://mlsanity.com/paper/2607.23886/) (2026)
- [VERDICT: Agreement Beats Pixel-Space Verification in Real-Document OCSR](https://mlsanity.com/paper/2608.22183/) (2026)
- [ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning](https://mlsanity.com/paper/2610.17183/) (2026)

## Abstract (arXiv)

Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex, thus robustly parsing them into structured data is an open challenge. In this paper, we present RxnScribe, a machine learning model for parsing reaction diagrams of varying styles. We formulate this structured prediction task with a sequence generation approach, which condenses the traditional pipeline into an end-to-end model. We train RxnScribe on a dataset of 1,378 diagrams and evaluate it with cross validation, achieving an 80.0% soft match F1 score, with significant improvements over previous models. Our code and data are publicly available at https://github.com/thomas0809/RxnScribe.