# MolMiner: You only look once for chemical structure recognition

arXiv: https://arxiv.org/abs/2205.11016 · HTML: https://mlsanity.com/paper/2205.11016/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 9 of 9)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Improving Generalization Performance by Switching from Adam to SGD](https://mlsanity.com/paper/1712.07628/) (2017)
- [ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning](https://mlsanity.com/paper/2002.09914/) (2020)

## Built on by (most cited first, 10 of 10)

- [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)
- [OpenChemIE: An Information Extraction Toolkit For Chemistry Literature](https://mlsanity.com/paper/2404.01462/) (2024)
- [MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild](https://mlsanity.com/paper/2411.11098/) (2024)
- [MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition](https://mlsanity.com/paper/2403.03691/) (2024)
- [Uni-Parser Technical Report](https://mlsanity.com/paper/2512.15098/) (2025)
- [RFL: Simplifying Chemical Structure Recognition with Ring-Free Language](https://mlsanity.com/paper/2412.07594/) (2024)
- [BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature](https://mlsanity.com/paper/2604.21508/) (2026)
- [One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images](https://mlsanity.com/paper/2311.14633/) (2023)
- [MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining](https://mlsanity.com/paper/2609.05807/) (2026)

## Abstract (arXiv)

Molecular structures are always depicted as 2D printed form in scientific documents like journal papers and patents. However, these 2D depictions are not machine-readable. Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades follow a rule-based approach where the key step of vectorization of the depiction is based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software MolMiner, which is primarily built up using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with distance-based construction algorithm. We carefully evaluate our software on four benchmark datasets with the state-of-the-art performance. Various real application scenarios are also tested, yielding satisfactory outcomes. The free download links of Mac and Windows versions are available: Mac: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/mac/PharmaMind-mac-latest-setup.dmg and Windows: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/win/PharmaMind-win-latest-setup.exe