ArticleCurrent microbiology2025
Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.
Article in Current microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Article
- Natural and Synthetic AMPs from Latin America across Diversity Engineering and Applications.ACS infectious diseases · 2026Review
- Dual-Mechanism Antimicrobial Peptides from a Nature-Inspired Scaffold.Journal of medicinal chemistry · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- Novel Hybrid Peptide ML-2 with Antibacterial and Antibiofilm Activity Against Pseudomonas aeruginosa.Current microbiology · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Resistance to antimicrobial agents has become a global threat, estimated to cause 10-million deaths annually by 2050. Antimicrobial peptides are emerging as an alternative and offer advantages over traditional antibiotics. Antimicrobial peptides generated by artificial intelligence (AI) strategies are potential alternatives that reduce costs and development time. This work optimized a set of peptides generated by two deep learning algorithms. The modifications made to the peptides were evaluated with bioinformatic and other AI tools as predictors of antimicrobial activity, hemolytic capacity, and toxicity. As a result, 26 synthetic peptides generated in silico were obtained with a high probability of being antimicrobial and biologically safe. Finally, 12 peptides were synthesized to perform in vitro tests against four bacterial species, Candida albicans, and cancer cells. Results indicate that 9 of the peptides have a MIC below 10 μM, and some have an inhibitory concentration at 2 μM, such as OrP1M for Escherichia coli, OrP9M for Pseudomonas aeruginosa, and VeP1 for Staphylococcus aureus. In addition, six peptides have activity against the breast cancer cell line (MCF-7), and peptide OrP1M had an IC
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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.