Validation of

Michael T Bergman, Fiona Mukherjee, Yufan Feng, Carol K Hall, Nicholas L Abbott

Abstract read

In one paragraph

Article in ACS central science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Michael T BergmanDepartment of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States.ORCID https://orcid.org/0000-0002-3716-5920

Fiona MukherjeeSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States.ORCID https://orcid.org/0000-0003-0391-8522

Yufan FengSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States.

Carol K HallDepartment of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States.ORCID https://orcid.org/0000-0002-7425-587X

Nicholas L AbbottSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States.ORCID https://orcid.org/0000-0002-9653-0326

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Solid-binding peptides (SBPs) are versatile molecules that can control a range of atomic-scale interfacial processes, but they remain challenging to discover. Current approaches for discovery rely on directed evolution, which samples only a small fraction of possible sequences. Data-driven methods for therapeutic peptides are also not applicable as they rely on crystal structures whereas peptides adopt varied conformations at solid surfaces. To address this challenge, we recently combined biophysical modeling and machine learning to design plastic-binding peptides that were predicted to have strong adsorption enthalpies. Here, we evaluate these designs using steered molecular dynamics and single-molecule force measurements and identify

Identifiers

PMID42369908

DOI10.1021/acscentsci.5c02193

PMCPMC13306600

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.