ArticleScientific reports2024
Structure-aware machine learning strategies for antimicrobial peptide discovery.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Potentials of Machine Learning in Predicting Key Features of Synthetic Antimicrobial Polymers.ACS polymers Au · 2026Article
- Deep learning-driven integrated pipeline for de novo design and synthesis of antimicrobial peptides.npj drug discovery · 2026Article
- Innovative Approaches to Combat Antimicrobial Resistance: A Review of Emerging Therapies and Technologies.Probiotics and antimicrobial proteins · 2026Review
- Redefining Therapies for Drug-Resistant Tuberculosis: Synergistic Effects of Antimicrobial Peptides, Nanotechnology, and Computational Design.Advanced healthcare materials · 2026Review
- Unstructured, disulfide-bridged C-terminus in helminth α-helical antimicrobial peptides enhances and modulates their activity.Scientific reports · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Dual-Channel Fluorescence Assays with Supramolecular Host-Dye Reporter Pairs for Membrane Activity Mapping of Peptides.Angewandte Chemie (International ed. in English) · 2026Article
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- IntelligentFrontiers in cellular and infection microbiology · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development.Frontiers in bioinformatics · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Archaeasins as a promising resource for developing next-generation antibiotics uncovered via deep learning.Engineering microbiology · 2025Article
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- Antimicrobial peptides: structure, functions and translational applications.Nature reviews. Microbiology · 2025Review
- A unified model of transient poration induced by antimicrobial peptides.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Immunomodulation in Non-traditional Therapies for Methicillin-resistant Staphylococcus aureus (MRSA) Management.Current microbiology · 2024Review
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2 authors.
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Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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