AMPBench-MT

AMPBench-MT is a homology-controlled benchmark for antimicrobial peptide endpoint prediction. The release is dated 2026-07-08.

The benchmark is organized around endpoint-aware prediction rather than binary AMP recognition alone. It contains processed task tables for AMP/non-AMP classification, species-aware MIC regression, activity spectrum classification, low-toxicity classification, HC50 hemolysis regression, selectivity regression, and a joint multitask endpoint table. All released peptide sequences use uppercase symbols from the 20 standard amino acids.

Authors

• Ziheng Zhou, Shanghai Ocean University, China, zihengzhouac@outlook.com
• Huiyu Luo, Tongji University, China, 2211285@tongji.edu.cn
• Xiaohu Zhu, Center for Safe AGI, China, aleph@csagi.org
• Nan Wang, DP Technology, China, wangnan411570@gmail.com
• Xuebiao Qin, Shanghai Ocean University, China, xbqin@shou.edu.cn
• Chaoyan Zhang, Shanghai Ocean University, China, chyzhang@shou.edu.cn
• Jun Yan, Shanghai Ocean University, China, yanjun@ieee.org (corresponding author)

Dataset Tasks

Config	Rows	Target	Main input fields	Splits
binary_amp	60,946	binary_label	sequence	train / validation / test
mic_regression	73,656	pmic_median	sequence, species	train / validation / test
mic_unit_sensitivity	65,658	pmic_median	sequence, species	all
spectrum	37,022	label_active	sequence, group_label	train / validation / test
spectrum_consensus	30,048	label_active	sequence, group_label	train / validation / test
low_toxicity	7,143	low_toxicity_label	sequence, cell_category	train / validation / test
hc50_hemolysis	1,595	pHC50_median	sequence, cell_category	train / validation / test
selectivity	9,774	log10_selectivity_index	sequence, species, cell_category	train / validation / test
joint_multitask	119,416	target	sequence, condition, task_name	train / validation / test

The all.csv files are retained for audit and convenience. For model evaluation, use the split files listed in the dataset card metadata. The mic_unit_sensitivity table is an all-only sensitivity table and was not used as a separate train/validation/test model-evaluation split.

File Layout

• data/<task>/all.csv: full released table for each task.
• data/<task>/train.csv, validation.csv, test.csv: homology-controlled splits for tasks with model-evaluation splits.
• metadata/task_overview.csv: task-level row counts, targets, inputs, and split sizes.
• metadata/schema.json: column-level schema and descriptions.
• metadata/split_summary.json: split and cluster summaries.
• metadata/source_databases.json: source database provenance and conservative source-term audit.
• metadata/validation_report.json: package-level validation flags for row counts, splits, sequence alphabet, and cluster disjointness.
• dataset_infos.json: Hugging Face style configuration metadata.
• MANIFEST.json and metadata/checksums.sha256: file inventory and SHA256 checksums.

Loading Examples

After upload to the Hub, use the repository id assigned to the private or public dataset repository. If the Hub account requires a namespace-qualified id, replace AMPBench-MT with the full repository id.

from datasets import load_dataset

REPO_ID = "AMPBench-MT"
mic = load_dataset(REPO_ID, "mic_regression")
mic_train = mic["train"]

The same tables can be loaded directly from the local release package:

import pandas as pd

mic_train = pd.read_csv("data/mic_regression/train.csv")
sequences = mic_train["sequence"]
species = mic_train["species"]
targets = mic_train["pmic_median"]

Split and Evaluation Protocol

AMPBench-MT uses MMseqs2 30% sequence-identity cluster splits with seed 42. The binary task uses the historical 10-50 amino-acid length range. Assay-derived endpoint tasks use the 5-100 amino-acid range and retain endpoint-specific observation units rather than forcing every peptide into every endpoint.

The benchmark should be evaluated with endpoint-appropriate metrics. Binary and multilabel-style classification tasks use threshold-aware and ranking-aware classification metrics. MIC, HC50, and selectivity regression use error and rank/correlation metrics. The joint multitask table records the corresponding single-task split in single_task_split and the multitask split in split.

Data Sources and Provenance

The processed tables derive from public peptide and antimicrobial peptide resources, including UniProtKB/Swiss-Prot, SATPdb, DRAMP 3.0, CAMPR4, DBAASP, dbAMP 3.0, AMPDB, GRAMPA, APD6, PEP-Lab, the EC-SA 2025 AMP regression collection, YADAMP, and DADP. Source names, evidence URLs, and source-term status are listed in metadata/source_databases.json.

Missing endpoint evidence is not converted into a negative, low-risk, or non-hemolytic label. A peptide appears in an endpoint only when the corresponding source evidence is present after the release cleaning rules.

License and Use

The Hugging Face metadata uses license: other because the benchmark is a compiled derivative package from multiple public sources with mixed, restrictive, or unclear source terms. The local package uses the AMPBench-MT Research and Review Use License in LICENSE.

The compiled benchmark is intended for non-commercial academic research, manuscript review, reproducible evaluation, and citation-backed benchmarking. Commercial use, broad redistribution, or relicensing of the compiled package should not be assumed from this release. Users must also comply with the terms of the original source databases listed in metadata/source_databases.json.

Limitations

AMPBench-MT is an in silico benchmark. It is not a wet-lab validation set and should not be interpreted as evidence that a peptide is a therapeutic candidate. The release standardizes unmodified canonical amino-acid sequences; it does not model noncanonical residues, post-translational modifications, formulation, delivery, pharmacokinetics, or clinical utility. The split protocol controls sequence identity at the cluster level but is not a source-held-out, species-held-out, publication-held-out, or external-database validation design. Protein language model and large language model pretraining corpora are not audited for overlap with AMPBench-MT records.

Citation

If you use AMPBench-MT, cite this dataset release. A paper citation can be added after the manuscript record is available.

@misc{ampbenchmt2026,
  title = {AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction},
  author = {Zhou, Ziheng and Luo, Huiyu and Zhu, Xiaohu and Wang, Nan and Qin, Xuebiao and Zhang, Chaoyan and Yan, Jun},
  year = {2026},
  note = {Dataset release dated 2026-07-08}
}