Datasets:
File size: 7,524 Bytes
fa66b08 d3f7429 fa66b08 36d6d32 dfd3542 d3f7429 dfd3542 36d6d32 dfd3542 36d6d32 dfd3542 d3f7429 dfd3542 d3f7429 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | ---
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}
}
``` |