README.md

---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
tags:
- chemistry
- supramolecular
- host-guest
- molecular-recognition
- llm-benchmark
pretty_name: 'SupraBench: Binding Affinity Prediction (BAP)'
dataset_info:
  features:
  - name: id
    dtype: string
  - name: task
    dtype: string
  - name: answer
    dtype: float64
  - name: host_name
    dtype: string
  - name: molecule
    dtype: string
  - name: version
    dtype: string
  - name: question
    dtype: string
  - name: source
    dtype: string
  - name: prompt_strategy
    dtype: string
  splits:
  - name: base
    num_bytes: 4589535
    num_examples: 2609
  - name: cot
    num_bytes: 4589535
    num_examples: 2609
  - name: fewshot
    num_bytes: 4589535
    num_examples: 2609
  download_size: 1533644
  dataset_size: 13768605
configs:
- config_name: default
  data_files:
  - split: base
    path: data/base-*
  - split: cot
    path: data/cot-*
  - split: fewshot
    path: data/fewshot-*
---


# 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:** [`arXiv:2606.13477`](https://arxiv.org/abs/2606.13477)
- 💻 **Code:** https://github.com/Tianyi-Billy-Ma/SupraBench
- 🤗 **All datasets:** 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 is partitioned by prompting strategy into three splits:
`base`, `cot`, and `fewshot` (the strategy is also recorded in the
`prompt_strategy` column). The CB[7] generalization records are a subset of every
split and remain identifiable by host name.

## Usage

```python
from datasets import load_dataset

# prompting-strategy split: "base" | "cot" | "fewshot"
ds = load_dataset("SupraBench/bap", split="cot")
print(ds[0]["question"])   # fully rendered prompt
print(ds[0]["answer"])     # reference label
```

To run the benchmark end-to-end (inference + scoring) see the
[code repository](https://github.com/Tianyi-Billy-Ma/SupraBench):

```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/
```

## 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},
  eprint        = {2606.13477},
  archivePrefix = {arXiv},
  journal = {arXiv preprint arXiv:2606.13477}
}
```