--- 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). - đŸ“„ **Paper:** [SupraBench: A Benchmark for Supramolecular Chemistry](https://huggingface.co/papers/2606.13477) - đŸ’» **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} } ```