Evidence map›Paper›PMID 41112193›Full record

ArticleACS bio & med chem Au2025

Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.

Miroslava Nedyalkova, Gozde Demirci, Youri Cortat, Kevin Schindler, Fatlinda Rahmani, Justine Horner, Mahdi Vasighi, Aurelien Crochet, Aleksandar Pavic, Olimpia Mamula and 2 more

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In one paragraph

Article in ACS bio & med chem Au, 2025. 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

12 authors.

Miroslava NedyalkovaDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.ORCID https://orcid.org/0000-0003-0793-3340
Gozde DemirciDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.
Youri CortatDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.
Kevin SchindlerDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.
Fatlinda RahmaniDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.
Justine HornerDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.
Mahdi VasighiDepartment of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran.
Aurelien CrochetDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.ORCID https://orcid.org/0000-0002-4763-2764
Aleksandar PavicInstitute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, Belgrade 11042, Serbia.ORCID https://orcid.org/0000-0003-3233-1341
Olimpia MamulaHaute Ecole d'Inginerie et d'Architecture Fribourg (HEIA), Institute of Chemical Technology, University of Applied Sciences Western Switzerland, Fribourg CH-1700, Switzerland.ORCID https://orcid.org/0000-0002-3538-3553
Fabio ZobiDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.ORCID https://orcid.org/0000-0002-9077-7184
Marco LattuadaDepartment of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.ORCID https://orcid.org/0000-0001-7058-9509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The escalating prevalence of antibiotic-resistant bacteria and the increasing complexity of managing severe infections emphasize the critical need for novel and effective antibiotics. Herein, we present a novel computational strategy focused on metal-based antibiotics, specifically rhenium (Re) complexes, for the rational design of next-generation antibacterial agents. Our approach integrates machine learning (ML) classification models to predict antibacterial potency, particularly against multidrug-resistant pathogens. A recognized limitation of conventional ML-driven antibiotic discovery is its dependence on structural similarity to known antibiotics, which hinders the exploration of structurally diverse and innovative antibiotic classes. To address this, we developed predictive ML models based on multi-layer perceptron (MLP) and random forest (RF) algorithms to estimate the minimum inhibitory concentration (MIC) of Re complexes against methicillin-resistant (MRSA) and methicillin-sensitive (MSSA)

Indexed as

antimicrobialantimicrobial resistancemachine learningmetal complexrhenium complexes

Identifiers

PMID41112193
PMCPMC12531868

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.