| --- |
| 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](https://dbaasp.org/). Used to train the CVAE generator and Judge predictor in |
| [Sloudis/controllable-amp-design](https://huggingface.co/Sloudis/controllable-amp-design) |
| ([GitHub repo](https://github.com/Sloudis/controllable-amp-design)). |
|
|
| **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](https://dbaasp.org/) and run `src/data/clean.py` from the GitHub repo. |
|
|
| ## Curation pipeline |
|
|
| Starting from raw DBAASP activity + peptide-metadata exports: |
| 1. Filter to MIC assays against *E. coli*, monomeric linear peptides with a known sequence |
| 2. Parse free-text MIC values (numbers, inequalities, ranges, ± notation) into a single float |
| 3. Standardize units to µM (µg/mL converted via computed monoisotopic molecular weight) |
| 4. Restrict to the 20 standard amino acids, length 5–50 |
| 5. Deduplicate by sequence, taking the geometric mean of MIC across repeated measurements |
| 6. 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 |
| |
| ```python |
| 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. |
| ``` |
|
|
