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Obtaining 3D Chemical Maps by Energy Filtered Transmission Electron Microscopy Tomography

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From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.

Alexey A Orlov¹, Tagir N Akhmetshin¹, Dragos Horvath¹

¹Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4, Blaise Pascal Str., 67000, Strasbourg, France.

Molecular Informatics

|December 5, 2024

Summary

This study evaluates dimensionality reduction techniques for analyzing chemical data. Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) were compared for their effectiveness in visualizing small molecules.

Keywords:

Generative Topographic Mapping Uniform Manifold Approximation and Projection chemical libraries chemical space chemography dimensionality reduction principal component analysis t-distributed Stochastic Neighbor Embedding

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Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Computational chemistry
Cheminformatics
Data science

Background:

Dimensionality reduction is crucial for analyzing high-dimensional chemical data.
Representing chemical structures as feature vectors enables analysis in lower-dimensional spaces.
Applications include chemical library analysis and visualization of chemical space.

Purpose of the Study:

To evaluate and compare common dimensionality reduction techniques.
To assess neighborhood preservation and visualization capabilities.
To analyze sets of small molecules from the ChEMBL database.

Main Methods:

Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Uniform Manifold Approximation and Projection (UMAP)
Generative Topographic Mapping (GTM)

Main Results:

Comparison of PCA, t-SNE, UMAP, and GTM performance.
Assessment of how well each method preserves neighborhood structures.
Evaluation of the clarity and interpretability of the resulting 2D/3D visualizations.

Conclusions:

The study provides insights into the strengths and weaknesses of different dimensionality reduction methods for chemical data.
Findings aid in selecting appropriate techniques for chemical library analysis and visualization.
Informed choices can enhance the exploration of chemical space and discovery of novel molecules.