Datasets:
metadata
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
- 💻 Code: GitHub Repository
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 for dependency management. You can run a task against a model using the following command found in the repository:
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
@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}
}