# MolMole: Molecule Mining from Scientific Literature

arXiv: https://arxiv.org/abs/2505.03777 · HTML: https://mlsanity.com/paper/2505.03777/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## 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)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [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)
- [MolGrapher: Graph-based Visual Recognition of Chemical Structures](https://mlsanity.com/paper/2308.12234/) (2023)
- [RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing](https://mlsanity.com/paper/2305.11845/) (2023)
- [OpenChemIE: An Information Extraction Toolkit For Chemistry Literature](https://mlsanity.com/paper/2404.01462/) (2024)

## Built on by (most cited first, 2 of 2)

- [MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition](https://mlsanity.com/paper/2608.03525/) (2026)
- [ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding](https://mlsanity.com/paper/2605.17214/) (2026)

## Abstract (arXiv)

The extraction of molecular structures and reaction data from scientific documents is challenging due to their varied, unstructured chemical formats and complex document layouts. To address this, we introduce MolMole, a vision-based deep learning framework that unifies molecule detection, reaction diagram parsing, and optical chemical structure recognition (OCSR) into a single pipeline for automating the extraction of chemical data directly from page-level documents. Recognizing the lack of a standard page-level benchmark and evaluation metric, we also present a testset of 550 pages annotated with molecule bounding boxes, reaction labels, and MOLfiles, along with a novel evaluation metric. Experimental results demonstrate that MolMole outperforms existing toolkits on both our benchmark and public datasets. The benchmark testset will be publicly available, and the MolMole toolkit will be accessible soon through an interactive demo on the LG AI Research website. For commercial inquiries, please contact us at hrefmailto:contactddu@lgresearch.aicontactddu@lgresearch.ai.