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Related Experiment Video

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Nested TMAPs to Visualize Billions of Molecules.

Alejandro Flores Sepúlveda1, Jean-Louis Reymond1

  • 1Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland.

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|May 4, 2026
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Summary
This summary is machine-generated.

This study introduces a novel framework for exploring massive chemical datasets, like the REAL database of 9.6 billion molecules. It enables efficient visualization and clustering of molecular structures using molecular quantum numbers (MQN) and tree-maps (TMAP).

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Area of Science:

  • Cheminformatics
  • Computational Chemistry
  • Data Visualization

Background:

  • Exploring large chemical datasets is computationally challenging.
  • Efficient methods are needed to navigate and understand molecular diversity.

Purpose of the Study:

  • To develop a visualization and clustering framework for billion-sized chemical data sets.
  • To enable efficient exploration of the REAL database of 9.6 billion molecules.

Main Methods:

  • Molecules represented by 42-dimensional molecular quantum numbers (MQN) fingerprints.
  • Clustering performed using Product Quantization and PQk-Means.
  • Tree-map (TMAP) visualization linking representatives and substructure similarity.

Main Results:

  • A framework for exploring billion-sized chemical data sets was successfully developed.
  • Nested TMAPs provide direct access to molecular structures within clusters.
  • The REAL database exploration is accessible via a web link.

Conclusions:

  • The presented framework offers an efficient method for exploring massive chemical datasets.
  • Nested TMAPs facilitate rapid navigation from cluster representatives to individual molecules.
  • This approach is computationally feasible on a single workstation.