Evidence map›Paper›PMID 42074338›Full record

ArticleInternational journal of molecular sciences2026

Machine Learning-Driven QSRR Modeling of Albumin Binding in Fluoroquinolones: An SVR Approach Supported by HSA Chromatography.

Yash Raj Singh, Wiktor Nisterenko, Joanna Fedorowicz, Jarosław Sączewski, Daniel Szulczyk, Katarzyna Ewa Greber, Wiesław Sawicki, Krzesimir Ciura

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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.

Yash Raj SinghDepartment of Physical Chemistry, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.ORCID 0000-0002-4664-1968
Wiktor NisterenkoDepartment of Physical Chemistry, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.ORCID 0009-0000-6903-1225
Joanna FedorowiczDepartment of Chemical Technology of Drugs, Faculty of Pharmacy, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.ORCID 0000-0001-7524-9909
Jarosław SączewskiDepartment of Organic Chemistry, Faculty of Pharmacy, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.ORCID 0000-0003-2966-7645
Daniel SzulczykChair and Department of Biochemistry, Medical University of Warsaw, 02-097 Warsaw, Poland.ORCID 0000-0003-3083-456X
Katarzyna Ewa GreberDepartment of Physical Chemistry, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.
Wiesław SawickiDepartment of Physical Chemistry, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.
Krzesimir CiuraDepartment of Physical Chemistry, Medical University of Gdansk, Al. Gen. J. Hallera 107, 80-416 Gdansk, Poland.ORCID 0000-0001-6187-6039

Funding

Gdańsk Medical University 01-66025National Centre for Research and Development LIDER//0066/L-15/2024National Science Centre 2024/55/D/NZ7/01853
6 · The paper itself

Abstract

Human serum albumin (HSA) binding critically influences drug distribution and pharmacokinetics. In this study, HSA affinity chromatography was integrated with machine-learning-based quantitative structure-retention relationship (QSRR) modeling to elucidate structural determinants of albumin binding in a library of 115 fluoroquinolone (FQs) derivatives. Experimentally determined log

Indexed as

FluoroquinolonesMachine LearningQuantitative Structure-Activity RelationshipSerum Albumin, HumanChromatography, AffinityHumansModels, MolecularProtein BindingSupport Vector MachineFluoroquinolonesSerum Albumin, Humanbiomimetic chromatographyfluoroquinoloneshuman serum albuminplasma protein bindingQSRRsupport vector regression

Identifiers

PMID42074338
PMCPMC13116993

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Registered trials

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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.