DBAASP V4: condition-resolved bioactivity and chemical structures for data-driven peptide and peptidomimetic discovery

Clicks: 3
ID: 337850
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,259 of 1,296 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,296 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The current peptide renaissance-driven by metagenomic and proteomic mining, molecular de-extinction, protein language models, and generative or evolutionary design-is producing millions of candidates, but useful machine learning depends on experimentally anchored, context-rich labels. The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is a manually curated resource linking peptides and peptidomimetics to quantitative antimicrobial, antibiofilm, anticancer, hemolytic, and mammalian-cell cytotoxicity measurements and their experimental conditions. DBAASP v4 contains >25 000 entries and extends the resource from a sequence-centered database to a structure-aware repository. Major additions include systematic SMILES representations, enhanced annotation of intrachain bonds and ring architectures, dedicated antibiofilm, and synergy records, PubChem links, >9200 molecular-dynamics trajectories, and new prediction services. The condition-resolved data support supervised learning, strain-, and target-specific models, model calibration, and surrogate fitness functions for genetic algorithms. SMILES representations further enable modeling of noncanonical peptides and peptidomimetics that cannot be adequately described by sequence alone. DBAASP therefore complements large peptide catalogues by providing the experimentally curated activity and structure layers, needed to convert sequence information into testable predictions, design, and optimization of biologically active compounds. DBAASP v4 is freely available at https://dbaasp.org.
Reference Key
openalex_W7225000368 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Malak Pirtskhalava, Evgenia Alimbarashvili, Maia Grigolava, Mindia Chubinidze, Boris Vishnepolsky, Giorgi Zaalishvili, Romeo Karaputadze, Natia Samsonidze, Alex Rosenthal, Darrell E. Hurt, Michael Tartakovsky, Andrei E. Gabrielian
Are you one of these authors? Claim your profile
Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1050
URL
https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkag1050/72639801/gkag1050.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.