Evidence map›Paper›PMID 41346856›Full record

ArticleFrontiers in artificial intelligence2025

Designing novel peptides with amyloid-

Vinod Kumar Yata, Om Pritam Das, Jarmani Dansana, Abhishikta Gadtya, Biswa Ranjan Meher, Sarad Pawar Naik Bukke, Narasaiah Kolliputi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 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. 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

7 authors.

Vinod Kumar YataDepartment of Biotechnology, School of Allied and Healthcare Sciences, Malla Reddy University, Hyderabad, Telangana, India.
Om Pritam DasDepartment of Molecular Biology, Central University of Andhra Pradesh, Ananthapuramu, Andhra Pradesh, India.
Jarmani DansanaComputational Biology and Bioinformatics Laboratory, PG Department of Botany, Berhampur University, Berhampur, Odisha, India.
Abhishikta GadtyaComputational Biology and Bioinformatics Laboratory, PG Department of Botany, Berhampur University, Berhampur, Odisha, India.
Biswa Ranjan MeherComputational Biology and Bioinformatics Laboratory, PG Department of Botany, Berhampur University, Berhampur, Odisha, India.
Sarad Pawar Naik BukkeDepartment of Pharmaceutics and Pharmaceutical Technology, Kampala International University, Western Campus, Ishaka, Uganda.
Narasaiah KolliputiDivision of Allergy and Immunology, Department of Internal Medicine, USF Morsani College of Medicine, Tampa, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence is transforming de novo biomolecular design, yet developing models that reliably generate functional, target-specific peptides remains a significant challenge. Here, we introduce and validate a novel two-stage Bidirectional Long Short-Term Memory (BiLSTM) framework for the generative design of short, functional peptides. Our AI pipeline is trained on full-length proteins annotated with specific Gene Ontology (GO) terms related to amyloid-

Indexed as

amyloid-βBiLSTMdeep learninggene ontology annotationmolecular dynamicspeptide design

Identifiers

PMID41346856
PMCPMC12673400

What Socratic holds

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Registered trials

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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.