Datasets:

UniParser
/

RxnMiner-Benchmark

pretty_name: ChemRxn-Bench (RxnMiner Benchmark)
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - chemistry
  - reaction-extraction
  - optical-chemical-structure-recognition
  - multimodal
  - benchmark
task_categories:
  - image-to-text
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet
extra_gated_heading: Request access to ChemRxn-Bench
extra_gated_description: >-
  The figures come from published articles and remain the property of their
  publishers. Access is granted for non-commercial research after review by the
  dataset maintainers.
extra_gated_button_content: Submit request
extra_gated_fields:
  Full name: text
  Affiliation: text
  Email (institutional): text
  Intended use: text
  I will use this dataset only for non-commercial research under its CC BY-NC-SA license: checkbox
  I will cite the RxnMiner paper in work that uses this dataset: checkbox

ChemRxn-Bench

🚀 Benchmark | 💻 GitHub | 📄 Paper

A benchmark for extracting complete, structured chemical reactions from literature figures: reaction schemes, substrate-scope tables with R-group templates, condition-optimization tables, multistep routes and total syntheses. Each figure comes with a manually annotated and expert-reviewed reference answer listing every concrete reaction drawn or implied in the figure, with exact molecular identities (SMILES, stereochemistry included), conditions and yields.

It accompanies the paper RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures (arXiv:2611.02826). Code: github.com/rxnminer/RxnMiner. The silver reaction corpus extracted by RxnMiner is released as UniParser/RxnMiner-Dataset.


Figures	603
Concrete reactions	8,510 (8,547 annotated records before the scorer's normalisation)
Generic templates (not scored)	675
Layouts	substrate scope 425, table 161, single step 6, complex figure 6, multistep / total synthesis 5
Difficulty	Very Hard 435, Hard 37, Medium 25, Easy 28, Very Easy 78

Data fields

Field	Type	Description
`id`	string	Figure id (stable across releases)
`image`	image	The figure, including its caption, table text and footnotes; the only model input
`annotation`	string (JSON)	Reference answer in the unified JSON format (below)
`layout_type`	string	`substrate_scope`, `table`, `single_step`, `complex_figure`, `multistep_or_total_synthesis`
`difficulty`	string	Difficulty tier, defined by how well Qwen3.8-Flash-Next performs on the figure (see the paper)
`num_reactions`	int	Concrete reactions in `annotation.selected_examples`
`num_templates`	int	Generic templates in `annotation.reaction_templates` (never scored)
`source`	string	Journal the figure was published in, or the public dataset it was collected from (ChemEAGLE, OpenChemIE)

Unified JSON (reference answer and expected prediction format)

{
  "reaction_templates": [ ... generic schemes with R groups (not scored) ... ],
  "selected_examples": [
    {
      "instance_id": "3a",
      "reactants":  [{"identifier": "1a", "smiles": "...", "bbox": [x0, y0, x1, y1]}],
      "products":   [{"identifier": "3a", "smiles": "..."}],
      "conditions": [[{"role": "catalyst", "text": "Pd(PPh3)4 (5 mol%)"}, {"role": "solvent", "text": "THF"}]],
      "metrics":    [{"role": "yield", "text": "82%"}]
    }
  ]
}

In the reference answers, molecules store their structure as E-SMILES (MolParser extended SMILES) or SMILES; predictions may use smiles, E-SMILES or SMILES (the scorer reads them in that order). Reagents printed over an arrow are recorded as condition text. Multistep routes list their stages under steps. Other fields in the reference answers (descriptions, footnotes, notes on conditions) document where each value comes from in the figure and are not scored.

Evaluation

The scorer is scorer/score_reaction_strict.py in this repository. It requires rdkit==2022.9.5; other RDKit versions can change canonical SMILES and therefore scores.

import importlib.util, json
from datasets import load_dataset
from huggingface_hub import hf_hub_download

path = hf_hub_download("UniParser/RxnMiner-Benchmark", "scorer/score_reaction_strict.py", repo_type="dataset")
spec = importlib.util.spec_from_file_location("score_reaction_strict", path)
S = importlib.util.module_from_spec(spec)
spec.loader.exec_module(S)

ds = load_dataset("UniParser/RxnMiner-Benchmark", split="test")
row = ds[0]
prediction = {...}                      # your system's unified JSON for row["image"]
result = S.score_both(prediction, json.loads(row["annotation"]))
print(result["reaction_f1_iso"])        # stereochemistry-aware; reaction_f1_ns ignores stereochemistry

From the root of a local copy of this repository: python scorer/score_reaction_strict.py prediction.json reference.json.

The concrete reactions in selected_examples are scored; each step of a multistep route counts as a separate reaction, and generic templates in reaction_templates are not scored. Reaction recall = fraction of reference products recovered; reaction precision = predicted reactions whose reactant set and product set both match a reference reaction exactly (after RDKit canonicalisation), divided by all predicted reactions; reaction F1 is computed per figure and averaged over figures (macro). A failed or missing prediction scores 0 and stays in the denominator. In the paper, RxnMiner with Qwen3.8-Flash-Next reaches 71.0 macro reaction F1 (stereochemistry-aware), versus 21.0 for the same model prompted directly.

License and use

The dataset is released under CC BY-NC-SA 4.0: non-commercial use, with attribution, and derivatives under the same license. The figures are reproduced from published articles for non-commercial research and remain the property of their publishers. Access requests are reviewed by the maintainers.

Citation

@misc{hu2026rxnminer,
  title         = {RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures},
  author        = {Hu, Jinbo and Zhang, Songren and Fu, Chuanxun and Huang, Jinrui and Wong, Zhen Hao and Cui, Dongxu and
                   Fan, Haoran and Ge, Muhao and Jian, Zhen and Zhou, Jian and Luo, Chengxiang and Lyu, Han and Tao, Haoyi and
                   Huang, Chaozheng and Li, Hanzheng and Wang, ZiChen and Ma, Hengtian and Dai, Jinyang and Wang, Nan and
                   Hong, Yanhui and Fang, Xi and Ke, Guolin},
  year          = {2026},
  eprint        = {2611.02826},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2611.02826}
}