ReviewBioorganic & medicinal chemistry2018
Machine learning-enabled discovery and design of membrane-active peptides.
Review in Bioorganic & medicinal chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed.
- Comparing Neural Networks and Naive Bayes in the Prediction of Drug Gene Interactions of Type 4 Collagenase for Gingival Epithelialization.International journal of dentistry · 2026Article
- Dynamics of Th1/Th17 responses and antimicrobial pathways in leprosy skin lesions.The Journal of clinical investigation · 2025Article
- Drp1 Proteins Released from Hydrolysis-Driven Scaffold Disassembly Trigger Nucleotide-Dependent Membrane Remodeling to Promote Scission.Journal of the American Chemical Society · 2025Article
- Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.Biomacromolecules · 2025Article
- Article
- peptidy: a light-weight Python library for peptide representation in machine learning.Bioinformatics advances · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.Nature chemistry · 2024Article
- An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.Physical chemistry chemical physics : PCCP · 2024Article
- Identification of Potent siRNA Delivery Peptides Using Computer Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Protein-based nanoparticles for therapeutic nucleic acid delivery.Biomaterials · 2024Review
- Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization.Scientific reports · 2024Article
- Rational design of stapled antimicrobial peptides.Amino acids · 2023Review
- In-Cell Penetration Selection-Mass Spectrometry Produces Noncanonical Peptides for Antisense Delivery.ACS chemical biology · 2023Article
- Incorporation of Non-Canonical Amino Acids into Antimicrobial Peptides: Advances, Challenges, and Perspectives.Applied and environmental microbiology · 2022Review
- Improvement of the Antibacterial Activity of Phage Lysin-Derived Peptide P87 through Maximization of Physicochemical Properties and Assessment of Its Therapeutic Potential.Antibiotics (Basel, Switzerland) · 2022Article
- PTML modeling for peptide discovery: in silico design of non-hemolytic peptides with antihypertensive activity.Molecular diversity · 2022Article
- Machine Learning in Antibacterial Drug Design.Frontiers in pharmacology · 2022Review
- Deep learning to design nuclear-targeting abiotic miniproteins.Nature chemistry · 2021Article
- Taxonomic bias in AMP prediction of invertebrate peptides.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen sequence space and guide experiment towards promising candidates with high putative activity. In this mini-review, we provide an introduction to antimicrobial peptides and summarize recent advances in machine learning-enabled antimicrobial peptide discovery and design with a focus on a recent work Lee et al. Proc. Natl. Acad. Sci. USA 2016;113(48):13588-13593. This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides. We use this model to discover membrane activity as a multiplexed function in diverse peptide families and provide interpretable understanding of the physicochemical properties and mechanisms governing membrane activity. Experimental validation of the classifier reveals it to have learned membrane activity as a unifying signature of antimicrobial peptides with diverse modes of action. Some of the discriminating rules by which it performs classification are in line with existing "human learned" understanding, but it also unveils new previously unknown determinants and multidimensional couplings governing membrane activity. Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action.
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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.