ArticleJournal of cheminformatics2020
Visualization of very large high-dimensional data sets as minimum spanning trees.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
130 citing papers in PubMed.
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- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- Nested TMAPs to Visualize Billions of Molecules.Journal of chemical information and modeling · 2026Article
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- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- Integrated QSAR, Molecular Docking, ADMET Profiling, and Antioxidant Evaluation of Substituted Chromone and Aryloxyalkanoic Acid Derivatives as Potential CysLTPharmaceuticals (Basel, Switzerland) · 2026Article
- Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints.Bioinformatics (Oxford, England) · 2026Article
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
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- Enzyformer: a two-stage pretrained model for enzymatic retrosynthesis.Journal of cheminformatics · 2026Article
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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- MITE: the Minimum Information about a Tailoring Enzyme database for capturing specialized metabolite biosynthesis.Nucleic acids research · 2026Article
- Reverse engineering molecules from fingerprints through deterministic enumeration and generative models.Journal of cheminformatics · 2025Article
- Natural Products Repository of Costa Rica (NAPRORE-CR): An Open-Access Database.Journal of chemical information and modeling · 2025Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation. Here, we propose a solution to this problem with a new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree (http://tmap.gdb.tools). Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL, FDB17, the Natural Products Atlas, DSSTox, as well as to the MoleculeNet benchmark collection of data sets. We also show its broad applicability with further examples from biology, particle physics, and literature.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.