Evidence map›Paper›PMID 40310992›Full record

ArticleBiomacromolecules2025

Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.

Diptomit Biswas, Sara Benson, Aidan Matunis, Gebremichal Gebretsadik, Adam Wertz, Ben J StPierre, Nathan Schacht, Yue Yan, Hanna Y Gebremichael, Pak Kin Wong and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomacromolecules, 2025. 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. Review
  2. Article
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

12 authors.

Diptomit BiswasDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Sara BensonDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Aidan MatunisHuck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania 16802, United States.
Gebremichal GebretsadikDepartment of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota 55455, United States.
Adam WertzDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Ben J StPierreDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Nathan SchachtDepartment of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota 55455, United States.
Yue YanDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Hanna Y GebremichaelDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Pak Kin WongDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Anthony D BaughnDepartment of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota 55455, United States.ORCID 0000-0003-1188-4238
Scott H MedinaDepartment of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.ORCID 0000-0001-5441-2164

Funding

Biomimetic Peptide Aerosols for Rapid Clearance of Pulmonary MDR TuberculosisR01AI165996 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI Scott Hammond Medina · 2022 to 2026
$2.2M
NIAID NIH HHS R01 AI165996
6 · The paper itself

Abstract

Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artificial intelligence (AI)-guided design can accelerate HDP discovery, but it traditionally requires large data sets to operationalize. We report an AI workflow that utilizes limited data sets (∼100 peptides) to uncover potent, selective, and safe HDPs by informing selection through lead candidate mutational scanning. This approach, referred to as Lead Informed Machine Interrogation of Therapeutic Sequences (LIMITS), is applied against the exemplary pathogen

Indexed as

Antimicrobial PeptidesAntitubercular AgentsArtificial IntelligenceMycobacterium tuberculosisPeptidesHumansMicrobial Sensitivity TestsAntimicrobial PeptidesAntitubercular AgentsPeptides

Identifiers

PMID40310992
PMCPMC12076502

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.