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

## 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.

## Key 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 to a spherical manifold to address known nonflat topology issues. The mathematical formalism of this new approach is described, and its ability to visualize in 2D electron density patterns and a tangible chemical library is showcased. © 2025 WILEY‐VCH GmbH