ArticleChemical science2021
Machine learning designs non-hemolytic antimicrobial peptides.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
What it found
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Who cites it
83 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Animal-Derived Antimicrobial Peptides: Emerging Therapeutic Strategies against Multidrug-Resistant Pathogens.Probiotics and antimicrobial proteins · 2026Review
- Nuclear Medicine Tracers Targeting Tumor-Associated Macrophages.Pharmaceutics · 2026Review
- Marine Antimicrobial Peptides: From Ocean Biodiversity to Genome Mining, Multi-Omics Discovery, and Biotechnological Innovation in the Battle Against Antimicrobial Resistance.Probiotics and antimicrobial proteins · 2026Review
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Diagnostics (Basel, Switzerland) · 2026Article
- A Comprehensive Review of Antimicrobial Peptides and Smart Biomaterials in Chronic Wound Therapy: Overcoming Biofilms, Resistance, and Translational Barriers.International journal of molecular sciences · 2026Review
- Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models.PLoS computational biology · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Controllable Protein Design by Prefix-Tuning Protein Language Models.Journal of chemical information and modeling · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Biochemical-knowledge-driven machine learning pipeline for generating potent antimicrobial peptides.Briefings in bioinformatics · 2026Article
- Pep5-Cpp, a Cyclin D2-Derived Antimicrobial.Molecules (Basel, Switzerland) · 2026Article
- The Future of Antibiotics and Artificial Intelligence: Some Thoughts from Discovery to Bedside.Infectious diseases and therapy · 2026Review
- Hybrid and conjugated antimicrobial peptides: new tactics to counter bacterial resistance.Frontiers in microbiology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Structural and mechanistic divergence in LL-37, HNP-1, and Magainin-2: An integrated computational and biophysical analysis.Current research in structural biology · 2025Article
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- MOFormer: navigating the antimicrobial peptide design space with Pareto-based multi-objective transformer.Briefings in bioinformatics · 2025Article
- Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.ACS bio & med chem Au · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against
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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.