Datasets:

Sloudis
/

controllable-amp-design-dataset

license: other
license_name: dbaasp-derived
license_link: https://dbaasp.org/
tags:
  - antimicrobial-peptides
  - bioinformatics
  - protein-sequences
size_categories:
  - 10K<n<100K

Controllable AMP Design — Curated E. coli MIC Dataset

10,044 curated antimicrobial peptide (AMP) sequences paired with a continuous minimum inhibitory concentration (MIC) activity score against E. coli, cleaned and deduplicated from DBAASP v3. Used to train the CVAE generator and Judge predictor in Sloudis/controllable-amp-design (GitHub repo).

This is the cleaned/derived dataset only. The raw DBAASP bulk exports it was built from are not redistributed here — their redistribution terms are unclear, so if you want to reproduce the cleaning pipeline from scratch, pull your own exports from dbaasp.org and run src/data/clean.py from the GitHub repo.

Curation pipeline

Starting from raw DBAASP activity + peptide-metadata exports:

Filter to MIC assays against E. coli, monomeric linear peptides with a known sequence
Parse free-text MIC values (numbers, inequalities, ranges, ± notation) into a single float
Standardize units to µM (µg/mL converted via computed monoisotopic molecular weight)
Restrict to the 20 standard amino acids, length 5–50
Deduplicate by sequence, taking the geometric mean of MIC across repeated measurements
log10-transform MIC

See src/data/clean.py in the GitHub repo for the exact implementation.

Columns

Column	Description
`sequence`	Peptide sequence (uppercase, standard 20 amino acids)
`mic_uM`	Minimum inhibitory concentration against E. coli, in µM
`log_mic`	log10(mic_uM)
`length`	Sequence length (residues)
`n_measurements`	Number of raw DBAASP measurements averaged (geometric mean) into this row
`stage`	`train` / `valid` / `test` split (stratified by log_mic decile, 80/10/10)

Stats

10,044 unique sequences — 8,044 train / 1,000 valid / 1,000 test
Sequence length: min 5, mean 19, max 50
MIC: min 0.005 µM, median 13.5 µM, max 18,000 µM
log10(MIC): mean 1.15, std 0.76 (train split)

Usage

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "Sloudis/controllable-amp-design-dataset",
    "amp_dataset.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

Citation

Cite the source database:

Pirtskhalava et al. "DBAASP v3: Database of antimicrobial/cytotoxic activity and structure
of peptides as a resource for development of new therapeutics." Nucleic Acids Research,
49(D1):D288-D297, 2021.

And, if you use this specific curated/cleaned version:

Stavros Loudis. "Controllable Antimicrobial Peptide Design via Conditional Variational
Autoencoders." Technical University of Crete, 2026.