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---
license: cc-by-4.0
task_categories:
- image-text-to-text
---
# SupraBench: A Benchmark for Supramolecular Chemistry
**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 provides a domain text corpus (SupraPMC) for domain-adaptive pretraining (DAPT).
- 📄 **Paper:** [SupraBench: A Benchmark for Supramolecular Chemistry](https://huggingface.co/papers/2606.13477)
- 💻 **Code:** [GitHub Repository](https://github.com/Tianyi-Billy-Ma/SupraBench)
## Tasks & Datasets
| Dataset | Task | Description |
|---|---|---|
| `bap` | Binding Affinity Prediction | Regress log $K_a$ for a host–guest pair |
| `tbs` | Top-Binder Selection | Pick the strongest binder among 4 candidate guests |
| `sid` | Solvent Identification | 6-way solvent classification from structure |
| `hgd` | Host-Guest Description | Open-ended QA on host/guest property profiles |
| `vqa` | Molecular Identification | Auxiliary vision task: identify a molecule from its image |
| `EU-PMC` | Text corpus | ~16M-token supramolecular corpus for DAPT |
| `Binding-Affinity` | Comprehensive anchor | Per-record binding data + host/guest SMILES, 2D, 3D, environment |
## Sample Usage
The benchmark uses [uv](https://docs.astral.sh/uv/) for dependency management. You can run a task against a model using the following command found in the repository:
```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/
```
## Citation
```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},
note = {arXiv preprint, link coming soon}
}
```