Evidence map›Paper›PMID 41015931›Full record

ArticleMacromolecular rapid communications2026

Uncovering Key Characteristics of Antibacterial Peptides through Machine Learning.

Jooyoung Roh, Cyrille Boyer, Priyank V Kumar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Macromolecular rapid communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jooyoung RohSchool of Chemical Engineering, University of New South Wales (UNSW), Sydney, New South Wales, Australia.
Cyrille BoyerSchool of Chemical Engineering, University of New South Wales (UNSW), Sydney, New South Wales, Australia.
Priyank V KumarSchool of Chemical Engineering, University of New South Wales (UNSW), Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-8203-7223

Funding

Australian Research Council
6 · The paper itself

Abstract

Antimicrobial peptides (AMPs) have emerged as promising alternatives to traditional antibiotics in addressing the growing threat of multi-drug-resistant (MDR) bacteria-a crisis that could lead to millions of deaths over the next three decades if left unaddressed. While the general role of cationic and hydrophobic interactions in AMP-mediated bacterial killing is well established, the distinctions between structural characteristics of AMPs targeting different types of bacteria remain underexplored. To address this issue and streamline the design of potent AMPs depending on the bacterial structure, machine learning (ML) models were employed on AMPs targeting Gram-negative bacteria (Pseudomonas aeruginosa PAO1), Gram-positive bacteria (Staphylococcus aureus ATCC 29213), and mycobacteria (Mycobacterium tuberculosis H37Rv and Mycobacterium smegmatis mc

Indexed as

Anti-Bacterial AgentsAntimicrobial PeptidesMachine LearningGram-Negative BacteriaHydrophobic and Hydrophilic InteractionsMicrobial Sensitivity TestsMycobacterium smegmatisPseudomonas aeruginosaStaphylococcus aureusAnti-Bacterial AgentsAntimicrobial Peptidesantibacterial polymersartificial intelligencecell structureclassificationgram‐negativegram‐positivemachine learningmycobacteriarandom forest

Identifiers

PMID41015931
PMCPMC13309147

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.