README.md

metadata

license: cc-by-4.0
task_categories:
  - other
tags:
  - chemistry
  - supramolecular-chemistry
  - llm-benchmark
dataset_info:
  features:
    - name: id
      dtype: string
    - name: task
      dtype: string
    - name: answer
      dtype: float64
    - name: host_name
      dtype: string
    - name: molecule
      dtype: string
    - name: version
      dtype: string
    - name: question
      dtype: string
    - name: source
      dtype: string
    - name: prompt_strategy
      dtype: string
  splits:
    - name: test
      num_bytes: 13768606
      num_examples: 7827
    - name: cb7
      num_bytes: 1082259
      num_examples: 651
  download_size: 1497145
  dataset_size: 14850865
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
      - split: cb7
        path: data/cb7-*

SupraBench

SupraBench is the first benchmark for evaluating large language models on supramolecular host-guest chemistry reasoning. It comprises four fundamental tasks plus an auxiliary vision task, and ships a domain text corpus for domain-adaptive pretraining (DAPT).

Supramolecular chemistry studies non-covalent host-guest assemblies that underpin drug delivery, chemical sensing, and in-vivo toxin sequestration. Designing host-guest systems is slow (days of dry-lab verification per pair); SupraBench probes whether LLMs can reason about these systems directly.

• 📄 Paper: SupraBench: A Benchmark for Supramolecular Chemistry
• 💻 Code: https://github.com/Tianyi-Billy-Ma/SupraBench
• 🤗 All datasets: https://huggingface.co/SupraBench

The dataset family

Dataset	Task	Description
SupraBench/bap	Binding Affinity Prediction	regress log K_a for a host-guest pair
SupraBench/tbs	Top-Binder Selection	pick the strongest binder among 4 candidate guests
SupraBench/sid	Solvent Identification	6-way solvent classification from structure
SupraBench/hgd	Host-Guest Description	open-ended QA on host/guest property profiles
SupraBench/EU-PMC	Text corpus	16M-token supramolecular corpus for DAPT
SupraBench/Binding-Affinity	Comprehensive anchor	per-record binding data + host/guest SMILES, 2D, 3D, environment

Each task dataset has a test split (merged records) and a cb7 split (the CB[7] supplement, for add-on evaluation). Each base/fewshot/cot rendering is tagged by the prompt_strategy field.

Sample Usage

As documented in the official repository, you can run a task against a model using the main.py entry point:

uv run python src/main.py \
    --task-config  configs/tasks/bap_base.yaml \
    --model-config configs/models/openrouter_qwen35_27b.yaml \
    --output-dir   outputs/

Dataset statistics

Task	# Samples
BAP	2,609
TBS	2,264
SID	2,172
HGD	135

Top-4 hosts (BAP / TBS / SID counts): CB[8] 261/200/571, CB[7] 217/200/217, beta-CD 201/200/264, p-SC4 144/144/225.

SupraCorpus (EU-PMC): 420,950 raw filtered articles -> 133,867 high-precision articles -> ~16M tokens.

Performance report

Main results from the SupraBench paper across the four fundamental tasks (8 LLMs x 3 prompting strategies). Bold = best, italic = second-best per column.

Base

Model	BAP MAE down	BAP RMSE down	TBS ACC up	TBS Regret down	SID F1 up	SID B.Acc up	HGD Recall up	HGD Prec up	HGD F1 up
Qwen3.5-9B	2.491	3.360	0.379	0.930	0.159	0.166	0.040	0.023	0.043
Qwen3.5-27B	1.803	2.503	0.404	0.851	0.225	0.364	0.495	0.072	0.122
Llama3.1-8B	2.699	3.630	0.228	1.281	0.151	0.225	0.266	0.059	0.092
Llama3.1-70B	1.632	2.149	0.338	1.054	0.118	0.254	0.487	0.091	0.152
GPT-5.4-Mini	1.549	2.182	0.428	0.810	0.219	0.274	0.437	0.086	0.137
GPT-5.4-Nano	1.642	2.169	0.411	0.816	0.182	0.347	0.472	0.062	0.107
Gemini-3-Flash	1.248	1.679	0.498	0.647	0.350	0.470	0.506	0.067	0.118
DeepSeek-v4	1.433	1.994	0.461	0.730	0.309	0.381	0.500	0.090	0.141

Sources & license

Binding records are derived from SupraBank (CC-BY-4.0); the text corpus is built from open-access Europe PMC articles subject to each article's individual license; molecular structures use PubChem and OPSIN.

The code and curated benchmark data are released under CC BY 4.0.

Citation

If you use SupraBench, please cite the paper and the upstream data sources.

@article{ma2026suprabench,
  title   = {SupraBench: A Benchmark for Supramolecular Host--Guest Chemistry Reasoning in Large Language Models},
  author  = {Ma, Tianyi and Ma, Yijun and Wang, Zehong and Sun, Weixiang and Li, Ziming and Schmidt, Connor R. and Zhang, Chuxu and Webber, Matthew J. and Ye, Yanfang},
  year    = {2026},
  journal = {arXiv preprint arXiv:2606.13477}
}