Evidence map›Paper›PMID 40521610›Full record

ArticleMolecular informatics2025

Spherical GTM: A New Proposition for Visualization of Chemical Data.

Farah Asgarkhanova, Marcou Gilles, Mikhail Volkov, Murielle Muzard, Richard Plantier-Royon, Caroline Rémond, Dragos Horvath, Alexandre Varnek

Abstract read
In one paragraph

Article in Molecular informatics, 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

8 authors.

Farah AsgarkhanovaLaboratory of Chemoinformatics, UMR 7140, University of Strasbourg, Strasbourg, France.
Marcou GillesLaboratory of Chemoinformatics, UMR 7140, University of Strasbourg, Strasbourg, France.ORCID https://orcid.org/0000-0003-1676-6708
Mikhail VolkovLaboratory of Chemoinformatics, UMR 7140, University of Strasbourg, Strasbourg, France.
Murielle MuzardUniversité de Reims Champagne-Ardenne, CNRS, ICMR, Reims, France.
Richard Plantier-RoyonUniversité de Reims Champagne-Ardenne, CNRS, ICMR, Reims, France.
Caroline RémondUniversité de Reims Champagne-Ardenne, INRAE, FARE, UMR 614, AFERE, Reims, France.
Dragos HorvathLaboratory of Chemoinformatics, UMR 7140, University of Strasbourg, Strasbourg, France.
Alexandre VarnekLaboratory of Chemoinformatics, UMR 7140, University of Strasbourg, Strasbourg, France.

Funding

Financial support was provided by the French National Research Agency ANR-22-CE43-0005
6 · The paper itself

Abstract

The Spherical Generative Topographic Mapping (SGTM) method represents an intuitive approach to visualize chemical data. Unlike the original Generative Topographic Mapping algorithm, which utilizes a bounded flat Euclidean space as a manifold, our proposed modification introduces a spherical manifold to address known nonflat topology issues. In this study, we describe the mathematical formalism of this new approach and showcase its ability to visualize 2D electron density patterns of water and benzene and the CosMoPoly chemical library-an enumeration of synthetically accessible molecules. By comparing the outcomes with established references, it is demonstrated that SGTM emerges as a novel 3D data visualization method, offering improved accuracy in the depiction of chemical structures.

Indexed as

AlgorithmsBenzeneSmall Molecule LibrariesWaterBenzeneSmall Molecule LibrariesWaterdata visualizationmanifold learningSpherical Generative Topographic Map

Identifiers

PMID40521610
PMCPMC12186103

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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.