| --- |
| license: cc-by-4.0 |
| task_categories: |
| - other |
| tags: |
| - chemistry |
| - supramolecular-chemistry |
| - science |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: task |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: correct_molecule |
| dtype: string |
| - name: options |
| list: string |
| - name: options_logka |
| list: float64 |
| - name: host_name |
| dtype: string |
| - name: version |
| dtype: string |
| - name: question |
| dtype: string |
| - name: source |
| dtype: string |
| - name: prompt_strategy |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 14349314 |
| num_examples: 6792 |
| - name: cb7 |
| num_bytes: 1249121 |
| num_examples: 600 |
| download_size: 2544347 |
| dataset_size: 15598435 |
| 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. |
|
|
| ## Links |
|
|
| - 📄 **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. |
|
|
| ## 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. Arrows give the optimization direction. |
|
|
| ### 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* | |
|
|
| ### Few-Shot |
|
|
| | 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 | 3.650 | 4.820 | 0.370 | 0.951 | 0.154 | 0.150 | 0.000 | 0.022 | 0.042 | |
| | Qwen3.5-27B | 2.258 | 3.256 | 0.392 | 0.889 | 0.178 | 0.257 | 0.636 | **0.585** | **0.580** | |
| | Llama3.1-8B | 5.504 | 6.940 | 0.283 | 1.227 | 0.142 | 0.182 | 0.655 | 0.369 | 0.456 | |
| | Llama3.1-70B | 1.774 | 2.359 | 0.354 | 1.026 | 0.144 | 0.185 | 0.631 | *0.474* | *0.531* | |
| | GPT-5.4-Mini | 1.958 | 2.808 | 0.430 | 0.824 | 0.141 | *0.291* | 0.542 | 0.228 | 0.307 | |
| | GPT-5.4-Nano | 2.176 | 2.894 | 0.419 | 0.819 | 0.190 | 0.270 | 0.532 | 0.095 | 0.152 | |
| | Gemini-3-Flash | **1.257** | **1.702** | **0.513** | **0.619** | **0.389** | **0.421** | *0.660* | 0.364 | 0.448 | |
| | DeepSeek-v4 | *1.618* | *2.276* | *0.470* | *0.713* | *0.203* | 0.225 | **0.720** | 0.303 | 0.352 | |
|
|
| ### CoT |
|
|
| | 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 | 3.664 | 4.885 | 0.382 | 0.944 | 0.167 | 0.197 | 0.300 | 0.039 | 0.068 | |
| | Qwen3.5-27B | 2.438 | 3.468 | 0.398 | 0.898 | 0.254 | *0.415* | **0.526** | 0.051 | 0.092 | |
| | Llama3.1-8B | 4.911 | 6.279 | 0.293 | 1.220 | 0.154 | 0.153 | 0.380 | **0.102** | **0.144** | |
| | Llama3.1-70B | 1.833 | 2.512 | 0.373 | 0.985 | 0.106 | 0.380 | 0.421 | 0.055 | 0.097 | |
| | GPT-5.4-Mini | 2.036 | 2.887 | 0.429 | 0.828 | 0.220 | 0.282 | 0.444 | 0.080 | 0.129 | |
| | GPT-5.4-Nano | 2.160 | 2.881 | 0.410 | 0.822 | 0.174 | 0.257 | 0.492 | 0.056 | 0.098 | |
| | Gemini-3-Flash | **1.261** | **1.723** | **0.510** | **0.609** | **0.331** | **0.432** | 0.512 | 0.062 | 0.110 | |
| | DeepSeek-v4 | *1.541* | *2.183* | *0.445* | *0.743* | *0.307* | 0.414 | *0.522* | *0.080* | *0.134* | |
|
|
| Takeaways: frontier proprietary LLMs (Gemini-3-Flash, DeepSeek-v4) lead the |
| quantitative tasks, yet every task leaves substantial headroom; no single |
| prompting strategy is universally best; and CoT amplifies rather than fixes the |
| underlying reasoning gap on binding-affinity prediction. |
|
|
| ## 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). |
|
|
| ## 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}, |
| note = {arXiv preprint, https://huggingface.co/papers/2606.13477} |
| } |
| ``` |
