ArticleNature communications2023
Discovering highly potent antimicrobial peptides with deep generative model HydrAMP.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 110 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
110 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Engineering short-sequence elements for condensate-like assemblies by de novo design.Synthetic and systems biotechnology · 2027Article
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics-Based Optimization with Limited Activity Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From sequence space to ecological function: microbiome-derived antimicrobial peptides as community effectors and therapeutic leads.Essays in biochemistry · 2026Review
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- Bridging linguistic reasoning and biophysical reality toward peptide engineering via instruction-tuned language modeling.Bioinformatics (Oxford, England) · 2026Article
- GANs in Peptide Drug Discovery: From De Novo Design to Multi-Property Optimization and Clinical Translation.Probiotics and antimicrobial proteins · 2026Review
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Diagnostics (Basel, Switzerland) · 2026Article
- Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.Nature communications · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery.PLoS computational biology · 2026Article
- Generative models for antimicrobial peptide design: auto-encoders and beyond.BioData mining · 2026Article
- Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.