Platinum(IV) compounds as potential drugs: a quantitative structure-activity relationship study.

Jurica Novak, Alena R Zykova, Vladimir A Potemkin, Vladimir V Sharutin, Olga K Sharutina

Abstract read

In one paragraph

Article in BioImpacts : BI, 2023. 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.

"Non-Classical" Platinum Complexes: A Concise Review.International journal of molecular sciences · 2025
Review

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

5 authors.

Jurica NovakDepartment of Biotechnology, University of Rijeka, Rijeka, Croatia.ORCID https://orcid.org/0000-0001-5744-6677

Alena R ZykovaFaculty of Chemistry, Department of Theoretical and Applied Chemistry, South Ural State University, Chelyabinsk, Russia.ORCID https://orcid.org/0000-0001-6333-5551

Vladimir A PotemkinDepartment of Biotechnology, University of Rijeka, Rijeka, Croatia.ORCID https://orcid.org/0000-0002-5244-8718

Vladimir V SharutinFaculty of Chemistry, Department of Theoretical and Applied Chemistry, South Ural State University, Chelyabinsk, Russia.

Olga K SharutinaFaculty of Chemistry, Department of Theoretical and Applied Chemistry, South Ural State University, Chelyabinsk, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Machine learning methods, coupled with a tremendous increase in computer power in recent years, are promising tools in modern drug design and drug repurposing. Methods: Machine learning predictive models, publicly available at chemosophia.com, were used to predict the bioactivity of recently synthesized platinum(IV) complexes against different kinds of diseases and medical conditions. Two novel QSAR models based on the BiS algorithm are developed and validated, capable to predict activities against the SARS-CoV virus and its RNA dependent RNA polymerase. Results: The internal predictive power of the QSAR models was tested by 10-fold cross-validation, giving cross-R Conclusion: Complexes

Indexed as

Drug repurposingGeneralized optimality criterionPlatinum(IV) complexesRNA dependent RNA polymerase inhibitorSARS-CoV

Identifiers

PMID37736338

DOI10.34172/bi.2023.24180

PMCPMC10509740

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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.