Evidence map›Paper›PMID 42453855›Full record

ArticleCell press blue2026

Self-driving lab for the data-driven design of single-chain polymer nanoparticles.

Alexander Suponya, Elena Di Mare, Cesar Ramírez, Apostolos Maroulis, Dylan Waynor, Cristian López, D Christopher Radford, Adam J Gormley

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell press blue, 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

8 authors.

Alexander SuponyaDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Elena Di MareDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Cesar RamírezDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Apostolos MaroulisDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Dylan WaynorDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Cristian LópezDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
D Christopher RadfordDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Adam J GormleyDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

Funding

Tuning multivalency for optimized ligand presentationR35GM138296 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI GORMLEY, ADAM JOSEPH · 2020 to 2024
$2.3M
NIGMS NIH HHS R35 GM138296
6 · The paper itself

Abstract

Rationally designed single-chain polymer nanoparticles (SCNPs) with self-assembling characteristics provide the necessary structural complexity for use as protein mimics while granting access to a broad chemical design space, straightforward preparation, and tunable properties. Here, we describe a high-throughput, autonomous workflow for active learning to discover structure-property relationships and iteratively predict and synthesize SCNPs. We developed a control system comprising a liquid-handling robot, a custom-built lightbox to catalyze photoinduced electron/energy transfer reverse addition-fragmentation chain transfer (PET-RAFT) polymerization, a dynamic light scattering (DLS) plate reader, and a robotic arm. We developed rationally designed, randomly sampled seed libraries as training sets for Gaussian process regressor (GPR) models. Over multiple generations of Bayesian optimization (BO), additional generations of polymer synthesis were found to improve model performance and represent the impact of specific monomer content on Rh. This automated polymer discovery platform serves as a useful prototype for designing SCNPs with structures tailored for biomedical applications.

Identifiers

PMID42453855
PMCPMC13367449

What OpenQuestion holds

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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.