Datasets:
File size: 5,582 Bytes
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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](https://huggingface.co/papers/2606.13477)
- 💻 **Code:** [https://github.com/Tianyi-Billy-Ma/SupraBench](https://github.com/Tianyi-Billy-Ma/SupraBench)
- 🤗 **All datasets:** [https://huggingface.co/SupraBench](https://huggingface.co/SupraBench)
## The dataset family
| Dataset | Task | Description |
|---|---|---|
| [`SupraBench/bap`](https://huggingface.co/datasets/SupraBench/bap) | Binding Affinity Prediction | regress log K_a for a host-guest pair |
| [`SupraBench/tbs`](https://huggingface.co/datasets/SupraBench/tbs) | Top-Binder Selection | pick the strongest binder among 4 candidate guests |
| [`SupraBench/sid`](https://huggingface.co/datasets/SupraBench/sid) | Solvent Identification | 6-way solvent classification from structure |
| [`SupraBench/hgd`](https://huggingface.co/datasets/SupraBench/hgd) | Host-Guest Description | open-ended QA on host/guest property profiles |
| [`SupraBench/EU-PMC`](https://huggingface.co/datasets/SupraBench/EU-PMC) | Text corpus | 16M-token supramolecular corpus for DAPT |
| [`SupraBench/Binding-Affinity`](https://huggingface.co/datasets/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:
```bash
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](https://suprabank.org/) (CC-BY-4.0); the text corpus is built from open-access [Europe PMC](https://europepmc.org/) articles subject to each article's individual license; molecular structures use [PubChem](https://pubchem.ncbi.nlm.nih.gov/) and [OPSIN](https://github.com/dan2097/opsin).
The code and curated benchmark data are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
## Citation
If you use SupraBench, please cite the paper and the upstream data sources.
```bibtex
@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}
}
``` |