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Datasets:
SupraBench
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tbs

Tasks:
Question Answering
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chemistry
supramolecular
host-guest
molecular-recognition
llm-benchmark
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tbs / README.md
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Card: base/cot/fewshot splits, add metadata + paper link + usage (supersedes PR)
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metadata 
license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
tags:
  - chemistry
  - supramolecular
  - host-guest
  - molecular-recognition
  - llm-benchmark
pretty_name: 'SupraBench: Top-Binder Selection (TBS)'
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: base
      num_bytes: 4783104
      num_examples: 2264
    - name: cot
      num_bytes: 4783104
      num_examples: 2264
    - name: fewshot
      num_bytes: 4783104
      num_examples: 2264
  download_size: 2384516
  dataset_size: 14349312
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

The dataset family

Dataset Task Description
SupraBench/bap Binding Affinity Prediction regress log K_a for a host-guest pair
SupraBench/tbs Top-Binder Selection pick the strongest binder among 4 candidate guests
SupraBench/sid Solvent Identification 6-way solvent classification from structure
SupraBench/hgd Host-Guest Description open-ended QA on host/guest property profiles
SupraBench/EU-PMC Text corpus 16M-token supramolecular corpus for DAPT
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 

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:

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 (CC-BY-4.0); the text corpus is built from open-access Europe PMC articles subject to each article's individual license; molecular structures use PubChem and OPSIN.

Citation

If you use SupraBench, please cite the paper and the upstream data sources.

@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}
}