Evidence map›Paper›PMID 41774122›Full record

ArticleJournal of medicinal chemistry2026

The Biggest Challenge for Prediction of Membrane Permeability of Cyclic Peptides: Scarce Data Availability.

Christina Lamers

Abstract read
In one paragraph

Article in Journal of medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Christina LamersInstitute for Drug Discovery, Leipzig University, Brüderstr. 34, 04103 Leipzig, Germany.ORCID 0000-0003-0962-8171

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AI-based multiparameter optimization for membrane permeability of cyclic peptides is hampered by the limited availability of ground truth biological data. This Viewpoint highlights the Featured Article reporting an AI model, integrating a high-throughput assay classifying billions of cyclic peptides for permeability-related characteristics. Combining this with a generative AI, the first de novo designed permeable cyclic peptides containing polar groups were reported.

Indexed as

Cell Membrane PermeabilityPeptides, CyclicAnimalsGenerative Artificial IntelligenceHigh-Throughput Screening AssaysHumansPeptides, Cyclic

Identifiers

PMID41774122
PMCPMC12990023

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.