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Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees

revised on 08.11.2019 and posted on 20.11.2019 by Daniel Probst, Jean-Louis Reymond

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 ( 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, DrugBank, 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.


NCCR TransCure - From transport physiology to identification of therapeutic targets

Swiss National Science Foundation

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University of Bern



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Declaration of Conflict of Interest

The authors declare no conflict of interest.