UniParser/MolParser-Mobile-V2

MolParser Mobile V2

💻 GitHub | 📘 E-SMILES 2.0 Spec | 📄 Report | 🚀 Demo

MolParser-Mobile-V2 is a lightweight Optical Chemical Structure Recognition (OCSR) model that converts molecular structure images directly into E-SMILES 2.0. It upgrades MolParser-Mobile for broader recognition of structures found in chemical literature, especially complex Markush structures, while retaining a compact 10M parameter architecture.

🚀 What's New

• E-SMILES 2.0 output with substantially broader coverage of literature molecules and Markush structures.
• Richer Markush type coverage for literature molecules, including atom- and ring-indexed substituents, explicit dummy attachments, nested substructures, structural repeating units and polymers, virtual arcs, colored endpoint balls, and axial-chirality annotations.
• 384 × 384 input resolution, increased from 224 × 224 in MolParser-Mobile.
• 384-token maximum output length, increased from 256 tokens.
• Improved recognition accuracy, particularly for complex and stereochemical structures.

For notation details, examples, validation, normalization, substitution, and rendering utilities, see the MolParser Repo and the E-SMILES specification.

📊 Performance

Accuracy for MolParser-Mobile-V2 was measured with FP16 inference, greedy decoding, and batch size 512. Deltas are relative to MolParser-Mobile.

Model	Parameters	Throughput (RTX 4090D)	Uni-Parser Bench	BioVista	WildMol-10k	USPTO
MolParser-Mobile	9.98M	1,520 Mol/s	0.823	0.801	0.734	0.836
MolParser-Mobile-V2	10.00M	1,296 Mol/s	0.850 (+0.027)	0.820 (+0.019)	0.762 (+0.028)	0.909 (+0.073)

⚡ Usage

Option 1. MolParser Library (Recommended)

The MolParser library provides a convenient interface for molecule detection, recognition, E-SMILES 2.0 post-processing, and rendering.

Clone the repository and install the package:

git clone https://github.com/dptech-corp/MolParser.git
cd MolParser
pip install -e .

Then run:

from molparser import MolParser

parser = MolParser(molparser_hf_repo="UniParser/MolParser-Mobile-V2", max_length=384) 

result = parser.parse("mol.png", rec_only=True)

To render the predicted E-SMILES as SVG or PNG, see Render E-SMILES:

from pathlib import Path
from molparser import utils as mutils

raw = "*C(O)c1cc(C(=O)N(*)*)cc(-c2*ccc*2)c1<sep><a>0:CF3</a><a>9:R[3]</a><a>10:R[2]</a><a>14:X</a><a>18:Y</a><r>1:R[1]?1-3</r>"
svg_text = mutils.draw(raw, output_format="svg")
Path("molecule.svg").write_text(svg_text, encoding="utf-8")

png_bytes = mutils.draw(raw, output_format="png")
Path("molecule.png").write_bytes(png_bytes)

Option 2. 🤗 Transformers

Load MolParser-Mobile-V2 directly with the Hugging Face transformers library.

import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor

repo_id = "UniParser/MolParser-Mobile-V2"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    repo_id,
    dtype=dtype,
    trust_remote_code=True,
).to(device).eval()

image = Image.open("mol.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(device, dtype=dtype) for k, v in inputs.items()}

output_ids = model.generate(**inputs, max_length=384, num_beams=1, do_sample=False)
caption = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
print(caption)

📜 License

MolParser-Mobile-V2 Weight

The MolParser-Mobile-V2 model weights are provided for non-commercial use only under CC BY-NC-SA 4.0.

For commercial licensing, please contact fangxi@dp.tech or open a discussion on Hugging Face.

MolParser Github Repo

The MolParser library (including E-SMILES post-processing and rendering) is available at https://github.com/dptech-corp/MolParser and is licensed under the Apache License 2.0, which permits commercial use, modification, and distribution, provided that the license and copyright notices are retained.

Note: Model weights, datasets, and third-party dependencies are subject to their respective licenses.

📖 Citation

If you use this model, please cite:

@article{fang2026molparserm,
  title={MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining},
  author={Fang, Xi and Lu, Haocheng and Lyu, Han and Luo, Chengxiang and Zhang, Linfeng and Ke, Guolin},
  journal={arXiv preprint arXiv:2609.05807},
  year={2026}
}

@inproceedings{fang2025molparser,
  title={Molparser: End-to-end visual recognition of molecule structures in the wild},
  author={Fang, Xi and Wang, Jiankun and Cai, Xiaochen and Chen, Shangqian and Yang, Shuwen and Tao, Haoyi and Wang, Nan and Yao, Lin and Zhang, Linfeng and Ke, Guolin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={24528--24538},
  year={2025}
}