ArticleNature biomedical engineering2023
Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences.
Article in Nature biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 1 of them a synthesis that pooled it.
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Who cites it
111 citing papers in PubMed, 1 synthesis or guideline pooled it, 239 citations in OpenAlex.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Interpretable machine learning framework for peptide lead optimization in ultralarge design spaces.npj drug discovery · 2026Article
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- Targeted Design of Novel Antimicrobial Peptides againstACS omega · 2026Article
- Antimicrobial peptides in anti-infective and immunomodulatory applications: Mechanisms, challenges, and emerging computational design.Zoological research · 2026Review
- Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.Nature communications · 2026Article
- PepMCP: a graph-based membrane contact probability predictor for membrane-lytic antimicrobial peptides.Bioinformatics (Oxford, England) · 2026Article
- Potentials of Machine Learning in Predicting Key Features of Synthetic Antimicrobial Polymers.ACS polymers Au · 2026Article
- QMAP: a benchmark for standardized evaluation of antimicrobial peptide MIC and hemolytic activity regression.Scientific reports · 2026Article
- Engineering host‑defense peptides enhanced by artificial intelligence and nano delivery systems to overcome biofilms and antimicrobial resistance.Engineering microbiology · 2026Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Antimicrobial peptides for bacterial infections and their biomedical applications.Discover nano · 2026Review
- Deep learning-driven integrated pipeline for de novo design and synthesis of antimicrobial peptides.npj drug discovery · 2026Article
- De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- AI-guided precise design of antimicrobial polymers through high-throughput screening technology on an automated platform.Bioactive materials · 2026Article
- Dual-pore size covalent organic frameworks shelled magnetic particles for anti-microbial peptide and microprotein enrichment with simultaneous exclusion of large proteins.Mikrochimica acta · 2026Article
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.Nature communications · 2026Article
- Uncovering evolutionarily remote and highly potent antimicrobial peptides with protein language models.Nature biomedical engineering · 2026Article
51 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.
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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.