Evidence map›Paper›PMID 41304455›Full record

ArticleToxics2025

Molecular Determinants of Per- and Polyfluoroalkyl Substances Binding to Estrogen Receptors.

Sahith Mada, Samuel Jordan, Joshua Mathew, Coby Loveranes, James Moran, Harrish Ganesh, Sivanesan Dakshanamurthy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Toxics, 2025. 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. Article
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

7 authors.

Sahith MadaCollege of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD 20742, USA.
Samuel JordanCollege of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD 20742, USA.
Joshua MathewCollege of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD 20742, USA.
Coby LoveranesCollege of Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
James MoranCollege of Arts & Sciences, Georgetown University, Washington, DC 20057, USA.
Harrish GaneshCollege of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA 22043, USA.
Sivanesan DakshanamurthyDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20007, USA.ORCID 0000-0003-3681-4121

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent organofluorines linked to cancer, organ dysfunction, and other health problems. This study used quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) modeling to examine the binding of PFAS to estrogen receptor alpha (ERα) and beta (ERβ). Molecular docking of 14,591 PFAS compounds was performed, and docking scores were used as a measure of receptor affinity. QSPR models were built for two datasets: the ERα and ERβ top binders (TBs), and a set of commonly exposed (CE) PFAS. These models quantified how chemical descriptors influence binding affinity. Across the models, higher density and electrophilicity indicated positive correlations with affinity, while surface tension indicated negative correlations. Electrostatic descriptors, including HOMO energy and positive Fukui index (F

Indexed as

binding affinityendocrine disruptionestrogen receptorshydrophobic interactionsmolecular dockingPFASQSPR and QSAR modeling

Identifiers

PMID41304455
PMCPMC12655904

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.