ReviewJACS Au2026
Harnessing AI for Antimicrobial Peptide Innovation against Multidrug Resistance.
Review in JACS Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Cyclotides from Plants Driving the Next Generation of Antibacterial Agents.Antibiotics (Basel, Switzerland) · 2026Review
- Bacillus-derived antimicrobial peptides as alternatives to antibiotics in poultry: mechanisms, applications, and future prospects- a review.Archives of microbiology · 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
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) poses a critical global health threat, demanding innovative strategies for drug discovery. Antimicrobial peptides (AMPs) represent promising alternatives, yet traditional experimental identification is limited by cost and scalability. Advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have transformed AMP discovery by enabling the accurate prediction, design, and optimization of novel candidates. This perspective highlights recent progress in AI-driven approaches, including predictive models and generative models, which accelerate large-scale peptide screening and functional annotation. We further emphasize the integration of multiomics data and the potential role of emerging technologies, such as quantum computing (QC), in overcoming computational bottlenecks for peptide design. Together, these approaches promise to expand the therapeutic landscape, paving the way toward next-generation peptide-based antimicrobials capable of circumventing resistance mechanisms and addressing urgent clinical needs.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.