Evidence map›Paper›PMID 42170081›Full record

ArticleRSC advances2026

Miguel A Maria-Solano, Raudah Lazim, Sun Choi

Abstract read
In one paragraph

Article in RSC advances, 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

3 authors.

Miguel A Maria-SolanoGlobal AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Science, Ewha Womans University 03760 Seoul Republic of Korea miguelangel.maria@uab.cat sunchoi@ewha.ac.kr.ORCID https://orcid.org/0000-0002-7837-0429
Raudah LazimGlobal AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Science, Ewha Womans University 03760 Seoul Republic of Korea miguelangel.maria@uab.cat sunchoi@ewha.ac.kr.
Sun ChoiGlobal AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Science, Ewha Womans University 03760 Seoul Republic of Korea miguelangel.maria@uab.cat sunchoi@ewha.ac.kr.ORCID https://orcid.org/0000-0002-7669-7954

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteolysis Targeting Chimeric Molecules (PROTACs) represent a promising avenue in drug discovery, as they can induce the targeted degradation of disease-relevant proteins within the cellular machinery. These compounds comprise a ligand tailored to bind the specific targeted protein connected to a recruiter molecule that engages with the E3 ligase. Despite their promise as therapeutic agents, the clinical advancement of these compounds has encountered substantial challenges, primarily due to the limited availability of suitable E3 ligases. Additionally, cell permeability and proteolytic stability, due to their peptide nature, often hinder their application. In this study, we developed a computational framework to model recruiters for the E3 ligase UBR1. This widely expressed protein has recently been demonstrated to be efficient in driving the degradation of oncogenic proteins. Our computational approach leverages a fragment-based peptidomimetics strategy, integrating pharmacophore filtering, docking, and fragment-linking optimization. Finally, we subjected the wild-type peptide and the most promising combined fragments to advanced binding free energy calculations, unveiling insights into their dynamic water-mediated binding mechanisms and their potential as robust E3 ligase UBR1 recruiters. This computational workflow is applicable to model other related PROTACs.

Identifiers

PMID42170081
PMCPMC13187912

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.