Evidence map›Paper›PMID 39456236›Full record

ReviewBiomolecules2024

Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs.

Nour Nissan, Mitchell C Allen, David Sabatino, Kyle K Biggar

Abstract readReview
In one paragraph

Review in Biomolecules, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Molecular dockingDigital discovery · 2026
    Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026
    Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Defatted chia (Current research in food science · 2025
    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

4 authors.

Nour NissanInstitute of Biochemistry, Departments of Biology & Chemistry, Carleton University, Ottawa, ON K1S 5B6, Canada.
Mitchell C AllenInstitute of Biochemistry, Departments of Biology & Chemistry, Carleton University, Ottawa, ON K1S 5B6, Canada.ORCID 0009-0000-7102-709X
David SabatinoInstitute of Biochemistry, Departments of Biology & Chemistry, Carleton University, Ottawa, ON K1S 5B6, Canada.ORCID 0000-0002-9797-0833
Kyle K BiggarInstitute of Biochemistry, Departments of Biology & Chemistry, Carleton University, Ottawa, ON K1S 5B6, Canada.

Funding

Natural Science and Engineering Research Council RGPIN-2023-04651
6 · The paper itself

Abstract

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.

Indexed as

Artificial IntelligenceDrug DiscoveryPeptidesAlgorithmsHumansPeptidesartificial intelligencecomputational toolsdrug discoverypeptide designpeptide drugsscreening

Identifiers

PMID39456236
PMCPMC11505729

What Socratic 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 Socratic graph.