Spherical GTM: A New Proposition for Visualization of Chemical Data
Farah Asgarkhanova, Gilles Marcou, Mikhail Volkov, Murielle Muzard, Richard Plantier‐Royon, Caroline Rémond, Dragos Horvath, Alexandre A. Varnek · Molecular Informatics · 2025
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.