Antimicrobial peptides (the alternate plural tag) covers the same field as #antimicrobial-peptide. Coverage runs across Peptilogics' biofilm work, Fedora's FPI-2119 lactivicin, Longhorn Vaccines' DRG5-BD11, and the HMD-AMP discovery platform from HLB Innovation.
The academic side has been productive. Groups at Birmingham (PEPITEM), Oxford, and others have published on AMP discovery, MRSA, and biofilm penetration. Nature Biomedical Engineering and Nature Biotechnology AMP papers continue to surface AI-discovered scaffolds.
For the singular form, see #antimicrobial-peptide. For the policy and resistance context, see #amr and #antibiotic-resistance.
A Frontiers in Bioinformatics review published March 17, 2026 from Tope Abraham Ibisanmi and colleagues at UNSW Sydney documents how computational antimicrobial peptide discovery has collapsed from decades to weeks. The review covers big-data mining, molecular dynamics simulations, and AI methods that capture complex sequence-activity relationships and predict novel AMPs from genomic and metagenomic data. The headline example: one large language model approach produced 18 de novo peptides of which 17 were active (94.4% hit rate) over a 48-day discovery cycle. The framing complements the broader AMP-as-AMR-response thesis with Aifeity, the University of Bonn, and Cesar de la Fuente at Penn — and lands as Cesar de la Fuente's Penn lab launches new generative AMR molecules into ESKAPE-pathogen testing.
A Frontiers in Medicine review published in 2026 consolidates the case for antimicrobial peptides (AMPs) as anticancer therapeutics and vaccine adjuvants. The cationic, amphipathic architecture that makes AMPs effective against bacterial membranes also enables selective electrostatic interactions with negatively charged malignant cell membranes — driving rapid membrane disruption and cell lysis. Beyond direct membrane effects, the review documents AMP-induced inhibition of DNA replication and protein synthesis, mitochondrial dysfunction, and tumor angiogenesis suppression. The piece also catalogs AMPs with adjuvant properties that boost vaccine immune responses against cancer and infectious disease. The work joins the Houston Methodist CAMPER MRSA paper, the Nature Communications few-shot Acinetobacter pipeline, and the Manchester penicillin-biosynthesis paper as part of the AMP wave through April–May 2026.
A Nature Communications paper describes a deep-learning pipeline that uses pre-trained protein language models combined with few-shot fine-tuning to identify antimicrobial peptides effective against Acinetobacter baumannii, a WHO critical-priority pathogen. The classification, ranking, and regression modules collaboratively prioritize candidates with high predicted activity, expanding the chemical space accessible to data-poor AMR targets. Lead candidates showed potent in vitro activity against carbapenem-resistant clinical isolates.
University of Nebraska Medical Center's Guangshun Wang lab released APD6, the expanded antimicrobial peptide database, containing 6,309 peptides (3,379 natural AMPs, 2,290 synthetic, 373 AI-predicted) as of January 2026. New features include the Antimicrobial Peptide Information Pipeline (AMPIP) and expanded functional wheel covering anticancer and antidiabetic activity — positioning APD6 as the most comprehensive reference for AMP drug discovery as AI-assisted antibiotic design accelerates.
A study published in Nature Microbiology used a generative AI approach to discover antimicrobial peptides effective against multidrug-resistant bacteria. The deep learning pipeline screened millions of candidate sequences, achieving a 94.4% success rate in lab validation, with two candidates showing exceptional efficacy and low resistance potential.