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
TL;DR
Researchers developed a new visualization method for chemical data using a spherical model, improving accuracy in representing molecular structures.
Contribution
The novel spherical manifold approach addresses nonflat topology issues in chemical data visualization.
Findings
SGTM successfully visualizes 2D electron density patterns of water and benzene.
The method accurately represents the CosMoPoly chemical library in 3D.
SGTM outperforms traditional methods in depicting chemical structures.
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. The Spherical Generative Topographic Mapping method extends the Generative Topographic Mapping method…
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Taxonomy
TopicsData Visualization and Analytics · Topological and Geometric Data Analysis · Image Retrieval and Classification Techniques